From 1007161328faf1cd8fa7c224fa850e7313d2d710 Mon Sep 17 00:00:00 2001 From: Clare Date: Wed, 14 Aug 2013 01:36:46 -0700 Subject: [PATCH 01/35] Create gh-pages branch via GitHub --- index.html | 86 ++++++++++++ javascripts/scale.fix.js | 17 +++ params.json | 1 + stylesheets/pygment_trac.css | 69 ++++++++++ stylesheets/styles.css | 255 +++++++++++++++++++++++++++++++++++ 5 files changed, 428 insertions(+) create mode 100644 index.html create mode 100644 javascripts/scale.fix.js create mode 100644 params.json create mode 100644 stylesheets/pygment_trac.css create mode 100644 stylesheets/styles.css diff --git a/index.html b/index.html new file mode 100644 index 00000000..a0bdde1c --- /dev/null +++ b/index.html @@ -0,0 +1,86 @@ + + + + + + The /Open Source/ Data Science Masters by datasciencemasters + + + + + + + +
+
+

The /Open Source/ Data Science Masters

+

Curriculum

+ +

View the Project on GitHub datasciencemasters/go

+ + + +
+
+

The Curriculum for learning Data Science, Open Source and at your fingertips.

+ +

+The Internet is Your Oyster

+ +

I didn't want to wait. I wanted to work on things I care about now. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?

+ +

With Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?

+ +

+The Motivation

+ +

We need more Data Scientists.

+ +
+

...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.

+
+ +

-- McKinsey Report Highlights the Impending Data Scientist Shortage 23 July 2013

+ +

+An Academic Shortfall

+ +

Classic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.

+ +
+

Academic credentials are important but not necessary for high-quality data science. The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.

+ +

We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available. Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.

+ +

And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.

+
+ +

-- James Kobielus, Closing the Talent Gap 17 Jan 2013

+ +

+The Open Source Curriculum

+ +

Start Here.

+ +

+Contribute

+ +

I'm not the expert -- please Share and Contribute. It's Open Source!

+ +

Follow me on Twitter @clarecorthell

+
+ +
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While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n## The Open Source Curriculum\r\n\r\n**[Start Here](http://datasciencemasters.org)**.\r\n\r\n## Contribute\r\n\r\nI'm not the expert -- please Share and Contribute. **It's Open Source**!\r\n\r\nFollow me on Twitter [@clarecorthell](http://twitter.com/clarecorthell)","google":"","note":"Don't delete this file! 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+ background: -o-linear-gradient(top, #f8f8f8 0%,#dddddd 100%); + background: -ms-linear-gradient(top, #f8f8f8 0%,#dddddd 100%); + background: linear-gradient(top, #f8f8f8 0%,#dddddd 100%); + + border-radius:5px; + border:1px solid #d2d2d2; + box-shadow:inset #fff 0 1px 0, inset rgba(0,0,0,0.03) 0 -1px 0; + width:270px; +} + +header li { + width:89px; + float:left; + border-right:1px solid #d2d2d2; + height:40px; +} + +header ul a { + line-height:1; + font-size:11px; + color:#999; + display:block; + text-align:center; + padding-top:6px; + height:40px; +} + +strong { + color:#222; + font-weight:700; +} + +header ul li + li { + width:88px; + border-left:1px solid #fff; +} + +header ul li + li + li { + border-right:none; + width:89px; +} + +header ul a strong { + font-size:14px; + display:block; + color:#222; +} + +section { + width:500px; + float:right; + padding-bottom:50px; +} + +small { + font-size:11px; +} + +hr { + border:0; + background:#e5e5e5; + height:1px; + margin:0 0 20px; +} + +footer { + width:270px; + float:left; + position:fixed; + bottom:50px; +} + +@media print, screen and (max-width: 960px) { + + div.wrapper { + width:auto; + margin:0; + } + + header, section, footer { + float:none; + position:static; + width:auto; + } + + header { + padding-right:320px; + } + + section { + border:1px solid #e5e5e5; + border-width:1px 0; + padding:20px 0; + margin:0 0 20px; + } + + header a small { + display:inline; + } + + header ul { + position:absolute; + right:50px; + top:52px; + } +} + +@media print, screen and (max-width: 720px) { + body { + word-wrap:break-word; + } + + header { + padding:0; + } + + header ul, header p.view { + position:static; + } + + pre, code { + word-wrap:normal; + } +} + +@media print, screen and (max-width: 480px) { + body { + padding:15px; + } + + header ul { + display:none; + } +} + +@media print { + body { + padding:0.4in; + font-size:12pt; + color:#444; + } +} From 2e2b52f784461554a83ec368536608ea90505650 Mon Sep 17 00:00:00 2001 From: Clare Corthell Date: Wed, 14 Aug 2013 02:10:32 -0700 Subject: [PATCH 02/35] CNAME --- CNAME | 1 + 1 file changed, 1 insertion(+) create mode 100644 CNAME diff --git a/CNAME b/CNAME new file mode 100644 index 00000000..0fb637a2 --- /dev/null +++ b/CNAME @@ -0,0 +1 @@ +datasciencemasters.org \ No newline at end of file From 33ddd6d0d7832c97af855045634eb2c22699aa67 Mon Sep 17 00:00:00 2001 From: Clare Corthell Date: Wed, 14 Aug 2013 02:18:38 -0700 Subject: [PATCH 03/35] rm CNAME --- CNAME | 1 - 1 file changed, 1 deletion(-) delete mode 100644 CNAME diff --git a/CNAME b/CNAME deleted file mode 100644 index 0fb637a2..00000000 --- a/CNAME +++ /dev/null @@ -1 +0,0 @@ -datasciencemasters.org \ No newline at end of file From 026cd0dce54442034b9890f8c17ed9dd58efb2c9 Mon Sep 17 00:00:00 2001 From: Clare Corthell Date: Wed, 14 Aug 2013 02:20:08 -0700 Subject: [PATCH 04/35] add README --- README.md | 37 +++++++++++++++++++++++++++++++++++++ 1 file changed, 37 insertions(+) create mode 100644 README.md diff --git a/README.md b/README.md new file mode 100644 index 00000000..0b0fb52c --- /dev/null +++ b/README.md @@ -0,0 +1,37 @@ +The Curriculum for learning Data Science, Open Source and at your fingertips. + +## The Internet is Your Oyster + +I didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today? + +With Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education? + +## The Motivation + +We need more Data Scientists. + +> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge. + +-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013 + +## An Academic Shortfall + +Classic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently. + +> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population. + +> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well. + +> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.** + +-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013 + +## The Open Source Curriculum + +**[Start Here](http://datasciencemasters.org)**. + +## Contribute + +I'm not the expert -- please Share and Contribute. **It's Open Source**! + +Follow me on Twitter [@clarecorthell](http://twitter.com/clarecorthell) \ No newline at end of file From 617a99c578be122606025206a0cb17c7930a31c3 Mon Sep 17 00:00:00 2001 From: Clare Date: Wed, 14 Aug 2013 03:07:27 -0700 Subject: [PATCH 05/35] Create gh-pages branch via GitHub From 56b965163052f3d35e389bc39bd28f7365b5f338 Mon Sep 17 00:00:00 2001 From: Clare Corthell Date: Wed, 14 Aug 2013 03:10:56 -0700 Subject: [PATCH 06/35] update top menu --- index.html | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/index.html b/index.html index a0bdde1c..529a1c7f 100644 --- a/index.html +++ b/index.html @@ -26,6 +26,10 @@

The /Open Source/ Data Science Masters

  • Download TAR Ball
  • View On GitHub
    • +
      +
    • tester menu item
      • +
    • tester menu item
      • +

    The Curriculum for learning Data Science, Open Source and at your fingertips.

    @@ -81,6 +85,6 @@

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doc[qsa]('meta[name=viewport]') : []; - - function fix() { - meta.content = 'width=device-width,minimum-scale=' + scales[0] + ',maximum-scale=' + scales[1]; - doc.removeEventListener(type, fix, true); - } - - if ((meta = meta[meta.length - 1]) && addEvent in doc) { - fix(); - scales = [.25, 1.6]; - doc[addEvent](type, fix, true); - } - -}; \ No newline at end of file +var metas = document.getElementsByTagName('meta'); +var i; +if (navigator.userAgent.match(/iPhone/i)) { + for (i=0; i ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n## An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n## The Open Source Curriculum\r\n\r\n**[Start Here](http://datasciencemasters.org)**.\r\n\r\n## Contribute\r\n\r\nI'm not the expert -- please Share and Contribute. **It's Open Source**!\r\n\r\nFollow me on Twitter [@clarecorthell](http://twitter.com/clarecorthell)","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The /Open Source/ Data Science Masters","tagline":"Curriculum","body":"The Curriculum for learning Data Science, Open Source and at your fingertips.\r\n\r\n## The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*See [My Curriculum](bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n## The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n## An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n## The Open Source Curriculum\r\n\r\n**[Start Here](http://datasciencemasters.org)**.\r\n\r\n## Contribute\r\n\r\nI'm not the expert -- please Share and Contribute. **It's Open Source**!\r\n\r\nFollow me on Twitter [@clarecorthell](http://twitter.com/clarecorthell)","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file diff --git a/stylesheets/styles.css b/stylesheets/styles.css index f14d9e46..dacf2e18 100644 --- a/stylesheets/styles.css +++ b/stylesheets/styles.css @@ -1,366 +1,220 @@ -@import url(https://fonts.googleapis.com/css?family=Arvo:400,700,400italic); - -/* MeyerWeb Reset */ - -html, body, div, span, applet, object, iframe, -h1, h2, h3, h4, h5, h6, p, blockquote, pre, -a, abbr, acronym, address, big, cite, code, -del, dfn, em, img, ins, kbd, q, s, samp, -small, strike, strong, sub, sup, tt, var, -b, u, i, center, -dl, dt, dd, ol, ul, li, -fieldset, form, label, legend, -table, caption, tbody, tfoot, thead, tr, th, td, -article, aside, canvas, details, embed, -figure, figcaption, footer, header, hgroup, -menu, nav, output, ruby, section, summary, -time, mark, audio, video { - margin: 0; - padding: 0; - border: 0; - font: inherit; - vertical-align: baseline; -} - - -/* Base text styles */ +@import url(https://fonts.googleapis.com/css?family=Lato:300italic,700italic,300,700); 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+ color:#39c; + font-weight:400; text-decoration:none; } -a:hover { - text-decoration: underline; -} - a small { - font-size: 12px; -} - -em { - font-style: italic; -} - -strong { - font-weight:700; -} - -ul li { - list-style: inside; - padding-left: 25px; + font-size:11px; + color:#777; + margin-top:-0.6em; + display:block; } -ol li { - list-style: decimal inside; - padding-left: 20px; +.wrapper { + width:860px; + margin:0 auto; } blockquote { - margin: 0; - padding: 0 0 0 20px; - font-style: italic; -} - -dl, dt, dd, dl p { - font-color: #444; -} - -dl dt { - font-weight: bold; -} - -dl dd { - padding-left: 20px; - font-style: italic; -} - -dl p { - padding-left: 20px; - font-style: italic; + border-left:1px solid #e5e5e5; + margin:0; + padding:0 0 0 20px; + font-style:italic; } -hr { - border:0; - background:#ccc; - height:1px; - margin:0 0 24px; -} - -/* Images */ - -img { - position: relative; - margin: 0 auto; - max-width: 650px; - padding: 5px; - margin: 10px 0 32px 0; - border: 1px solid #ccc; -} - - -/* Code blocks */ - code, pre { - font-family: Monaco, "Bitstream Vera Sans Mono", "Lucida Console", Terminal, monospace; - color:#000; - font-size:14px; + font-family:Monaco, Bitstream Vera Sans Mono, Lucida Console, Terminal; + color:#333; + font-size:12px; } pre { - padding: 4px 12px; - background: #FDFEFB; - border-radius:4px; - border:1px solid #D7D8C8; - overflow: auto; - overflow-y: hidden; - margin-bottom: 32px; + padding:8px 15px; + background: #f8f8f8; + border-radius:5px; + border:1px solid #e5e5e5; + overflow-x: auto; } - -/* Tables */ - table { width:100%; + border-collapse:collapse; } -table { - border: 1px solid #ccc; - margin-bottom: 32px; - text-align: left; - } - -th { - font-family: 'Arvo', Helvetica, Arial, sans-serif; - font-size: 18px; - font-weight: normal; - padding: 10px; - background: #232323; - color: #FDFEFB; - } - -td { - padding: 10px; - background: #ccc; - } - - -/* Wrapper */ -.wrapper { - width:960px; -} - - -/* Header */ - -header { - background-color: #171717; 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+ padding:0; + + background: #eee; + background: -moz-linear-gradient(top, #f8f8f8 0%, #dddddd 100%); + background: -webkit-gradient(linear, left top, left bottom, color-stop(0%,#f8f8f8), color-stop(100%,#dddddd)); + background: -webkit-linear-gradient(top, #f8f8f8 0%,#dddddd 100%); + background: -o-linear-gradient(top, #f8f8f8 0%,#dddddd 100%); + background: -ms-linear-gradient(top, #f8f8f8 0%,#dddddd 100%); + background: linear-gradient(top, #f8f8f8 0%,#dddddd 100%); + + border-radius:5px; + border:1px solid #d2d2d2; + box-shadow:inset #fff 0 1px 0, inset rgba(0,0,0,0.03) 0 -1px 0; + width:270px; } header li { - list-style-type: none; - width:132px; - height:15px; - margin-bottom: 12px; - line-height: 1em; - padding: 6px 6px 6px 7px; - - background: #AF0011; - background: -moz-linear-gradient(top, #AF0011 0%, #820011 100%); - background: -webkit-gradient(linear, left top, left bottom, color-stop(0%,#f8f8f8), color-stop(100%,#dddddd)); - background: -webkit-linear-gradient(top, #AF0011 0%,#820011 100%); 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+ text-align:center; + padding-top:6px; + height:40px; } -a.buttons { - -webkit-font-smoothing: antialiased; - background: url(../images/arrow-down.png) no-repeat; - font-weight: normal; - text-shadow: rgba(0, 0, 0, 0.4) 0 -1px 0; - padding: 2px 2px 2px 22px; - height: 30px; +strong { + color:#222; + font-weight:700; } -a.github { - background: url(../images/octocat-small.png) no-repeat 1px; +header ul li + li { + width:88px; + border-left:1px solid #fff; } -a.buttons:hover { - color: #fff; - text-decoration: none; +header ul li + li + li { + border-right:none; + width:89px; } - -/* Section - for main page content */ +header ul a strong { + font-size:14px; + display:block; + color:#222; +} section { - width:650px; + width:500px; float:right; padding-bottom:50px; } +small { + font-size:11px; +} -/* Footer */ +hr { + border:0; + background:#e5e5e5; + height:1px; + margin:0 0 20px; +} footer { - width:170px; + width:270px; float:left; position:fixed; - bottom:10px; - padding-left: 50px; + bottom:50px; } @media print, screen and (max-width: 960px) { - + div.wrapper { width:auto; margin:0; } - + header, section, footer { float:none; position:static; width:auto; } - - footer { - border-top: 1px solid #ccc; - margin:0 84px 0 50px; - padding:0; - } - + header { padding-right:320px; } - + section { - padding:20px 84px 20px 50px; + border:1px solid #e5e5e5; + border-width:1px 0; + padding:20px 0; margin:0 0 20px; } - + header a small { display:inline; } - + header ul { position:absolute; - right:130px; - top:84px; + right:50px; + top:52px; } } @@ -368,40 +222,28 @@ footer { body { word-wrap:break-word; } - + header { - padding:10px 20px 0; - margin-right: 0; + padding:0; } - - section { - padding:10px 0 10px 20px; - margin:0 0 30px; - } - - footer { - margin: 0 0 0 30px; - } - + header ul, header p.view { position:static; } + + pre, code { + word-wrap:normal; + } } @media print, screen and (max-width: 480px) { - - header ul li.download { + body { + padding:15px; + } + + header ul { display:none; } - - footer { - margin: 0 0 0 20px; - } - - footer a{ - display:block; - } - } @media print { @@ -410,4 +252,4 @@ footer { font-size:12pt; color:#444; } -} \ No newline at end of file +} From 9f8fa0b185dd964cb24aa3ce8e7d70b5d77be949 Mon Sep 17 00:00:00 2001 From: Clare Date: Fri, 16 Aug 2013 19:50:18 -0700 Subject: [PATCH 10/35] Create gh-pages branch via GitHub --- index.html | 2 +- params.json | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/index.html b/index.html index 547e0883..23689b24 100644 --- a/index.html +++ b/index.html @@ -35,7 +35,7 @@

    I didn't want to wait. I wanted to work on things I care about now. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?

    -

    See My Curriculum

    +

    See My Curriculum

    With Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?

    diff --git a/params.json b/params.json index 3996c2f0..8594e76f 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"The /Open Source/ Data Science Masters","tagline":"Curriculum","body":"The Curriculum for learning Data Science, Open Source and at your fingertips.\r\n\r\n## The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*See [My Curriculum](bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n## The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n## An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n## The Open Source Curriculum\r\n\r\n**[Start Here](http://datasciencemasters.org)**.\r\n\r\n## Contribute\r\n\r\nI'm not the expert -- please Share and Contribute. **It's Open Source**!\r\n\r\nFollow me on Twitter [@clarecorthell](http://twitter.com/clarecorthell)","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The /Open Source/ Data Science Masters","tagline":"Curriculum","body":"The Curriculum for learning Data Science, Open Source and at your fingertips.\r\n\r\n## The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*See [My Curriculum](http://bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n## The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n## An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n## The Open Source Curriculum\r\n\r\n**[Start Here](http://datasciencemasters.org)**.\r\n\r\n## Contribute\r\n\r\nI'm not the expert -- please Share and Contribute. **It's Open Source**!\r\n\r\nFollow me on Twitter [@clarecorthell](http://twitter.com/clarecorthell)","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file From f330a48a9809a5c0f479fdca7d4271e84a91ceca Mon Sep 17 00:00:00 2001 From: Clare Date: Fri, 16 Aug 2013 21:44:13 -0700 Subject: [PATCH 11/35] Create gh-pages branch via GitHub --- fonts/copse-regular-webfont.eot | Bin 0 -> 123680 bytes fonts/copse-regular-webfont.svg | 247 ++++ fonts/copse-regular-webfont.ttf | Bin 0 -> 123504 bytes fonts/copse-regular-webfont.woff | Bin 0 -> 46152 bytes fonts/quattrocentosans-bold-webfont.eot | Bin 0 -> 54776 bytes fonts/quattrocentosans-bold-webfont.svg | 247 ++++ fonts/quattrocentosans-bold-webfont.ttf | Bin 0 -> 54564 bytes fonts/quattrocentosans-bold-webfont.woff | Bin 0 -> 27880 bytes fonts/quattrocentosans-bolditalic-webfont.eot | Bin 0 -> 62100 bytes fonts/quattrocentosans-bolditalic-webfont.svg | 248 ++++ fonts/quattrocentosans-bolditalic-webfont.ttf | Bin 0 -> 61860 bytes .../quattrocentosans-bolditalic-webfont.woff | Bin 0 -> 31096 bytes 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zue`K0O?)g1>7Tzt@7Qe_v;&ZVL8E~Z!o8zI&uO9&@~0~af3~%?4JI!v45o^zNj^`@ z=?_0U$(3SG##Urh7SxhR3~so`>$)oEALXuU-jy%4^#xP**XfqT_fm1^fr*$ zR!P96R$0#LwmP!i<=B-|eHeM-UlNJt;V%+)FXXBpx?oHJ|dpL3TODzPVG-y6MkLHAXXzd2`xQw?KJZ`7x^Ff*sB96F5%w3axVOQfQ}I%Ng)Di=d59$K&}ELE zc|2GXuyRT!;muf>ot-Q2qBEj?HIk-R!hEros?y3GjdOpXXMd%~ro4k+T*2?aNcW`o z_d85Oaw5jITAh%SW$K$X54?7F3+%y4iViC(Y%hog&SDxXy|tt)goN$6fEOgiMD5Dy zyp6fp+=ukAjrH^oAA+tL?mVd(z!q zL8Rn%yN9*i+vQBDukRzy+y5ahYE7z*3OHI(U87`?0n?(@_w2I>?5W_q+@=`4_mFz4 z_M7silscZo0kE#_r7KbFFM{iG*R7Myw|L?W*)UQZK--y6lvjT@p?IBBCQ%wG4h8H{ WV+R-9@!R~r2jHw6u(vQ?(f The /Open Source/ Data Science Masters by datasciencemasters - - + + + + -

    +

    The /Open Source/ Data Science Masters

    Curriculum

    +
    -

    View the Project on GitHub datasciencemasters/go

    + - - +
    +

    The Curriculum for learning Data Science, Open Source and at your fingertips.

    @@ -65,24 +76,171 @@

    -- James Kobielus, Closing the Talent Gap 17 Jan 2013

    -

    -The Open Source Curriculum

    - -

    Start Here.

    - -

    +

    Ready?

    + +

    +The Open Source Data Science Curriculum

    + +
      +
    • +Intro to Data Science UW / Coursera + +
        +
      • +Topics: Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.
        • +
      +
      • +

    +Math

    + +

    +Computing

    + +

    +Capstone Project

    + +
      +
    • Toy Data Ideas
      • +
    • Capstone Analysis of Your Own Design + +
        +
      • (ideas?)
        • +
      +
      • +

    +Further Study Resources:

    + +

    Contribute

    I'm not the expert -- please Share and Contribute. It's Open Source!

    -

    Follow me on Twitter @clarecorthell

    +

    Take a look at my transcript; Please showcase your transcript on the wiki too!

    + +

    Follow me on Twitter @clarecorthell

    - + \ No newline at end of file diff --git a/javascripts/main.js b/javascripts/main.js new file mode 100644 index 00000000..c57e54c3 --- /dev/null +++ b/javascripts/main.js @@ -0,0 +1,53 @@ +var sectionHeight = function() { + var total = $(window).height(), + $section = $('section').css('height','auto'); + + if ($section.outerHeight(true) < total) { + var margin = $section.outerHeight(true) - $section.height(); + $section.height(total - margin - 20); + } else { + $section.css('height','auto'); + } +} + +$(window).resize(sectionHeight); + +$(document).ready(function(){ + $("section h1, section h2").each(function(){ + $("nav ul").append("
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I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*See [My Curriculum](http://bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n## The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n## An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n## The Open Source Curriculum\r\n\r\n**[Start Here](http://datasciencemasters.org)**.\r\n\r\n## Contribute\r\n\r\nI'm not the expert -- please Share and Contribute. **It's Open Source**!\r\n\r\nFollow me on Twitter [@clarecorthell](http://twitter.com/clarecorthell)","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The /Open Source/ Data Science Masters","tagline":"Curriculum","body":"The Curriculum for learning Data Science, Open Source and at your fingertips.\r\n\r\n## The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*See [My Curriculum](http://bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n## The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n## An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n**Ready?**\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\n* **Intro to Data Science** [UW / Coursera](https://www.coursera.org/course/datasci)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n### Math\r\n* Linear Algebra / Levandosky [Stanford / Book](http://www.amazon.com/Linear-Algebra-Steven-Levandosky/dp/0536667470/ref=sr_1_1?ie=UTF8&qid=1376546498&sr=8-1&keywords=linear+algebra+levandosky#)\r\n* Statistics [Stats in a Nutshell / Book](http://shop.oreilly.com/product/9780596510497.do)\r\n* Problem-Solving Heuristics \"How To Solve It\" [Polya / Book](http://en.wikipedia.org/wiki/How_to_Solve_It)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](https://www.coursera.org/course/algo)\r\n * Algorithm Design [Kleinberg & Tardos / Book](http://www.amazon.com/Algorithm-Design-Jon-Kleinberg/dp/0321295358/ref=sr_1_1?ie=UTF8&qid=1376702127&sr=8-1&keywords=kleinberg+algorithms)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Coursera](https://www.coursera.org/course/db)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Book](http://i.stanford.edu/~ullman/mmds.html)\r\n * Mining The Social Web [O'Reilly / Book](http://shop.oreilly.com/product/0636920010203.do)\r\n * Introduction to Information Retrieval [Stanford / Book](http://nlp.stanford.edu/IR-book/information-retrieval-book.html)\r\n\r\n* **Machine Learning**\r\n * Machine Learning / Ng [Stanford / Coursera](https://www.coursera.org/course/ml)\r\n * Programming Collective Intelligence [O'Reilly / Book]()\r\n * Statistics [The Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) #current\r\n\r\n* **Probabilistic Graphical Models**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)\r\n * PGMs / Koller [Stanford / Coursera](https://www.coursera.org/course/pgm)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python [O'Reilly / Book](http://shop.oreilly.com/product/9780596516499.do)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [O'Reilly / Book](http://www.kqzyfj.com/click-7040302-11260198?url=http%3A%2F%2Fshop.oreilly.com%2Fproduct%2F0636920023784.do&cjsku=0636920023784)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://blogs.ischool.berkeley.edu/i290-abdt-s12/)\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](https://www.coursera.org/course/networksonline)\r\n * Information Visualization [\"Envisioning Information\" Tufte / Book](http://www.amazon.com/Envisioning-Information-Edward-R-Tufte/dp/0961392118/ref=sr_1_8?ie=UTF8&qid=1376709039&sr=8-8&keywords=information+design)\r\n\r\n* **Python** (Learning)\r\n * New To Python: [Learn Python the Hard Way](http://learnpythonthehardway.org/), [Google's Python Class](code.google.com/edu/languages/google-python-class/)\r\n\r\n* Python (Libraries)\r\n * Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://www.lowindata.com/2013/installing-scientific-python-on-mac-os-x/)\r\n * Bayesian Inference | [pymc](https://github.com/pymc-devs/pymc)\r\n * Labeled data structures objects, statistical functions, etc [pandas](https://github.com/pydata/pandas) (See: Python for Data Analysis)\r\n * Python wrapper for the Twitter API [twython](https://github.com/ryanmcgrath/twython)\r\n * Tools for Data Mining & Analysis [scikit-learn](http://scikit-learn.org/stable/)\r\n * Network Modeling & Viz [networkx](http://networkx.github.io/)\r\n * Natural Language Toolkit [NLTK](http://nltk.org/)\r\n\r\n### Capstone Project\r\n* [Toy Data Ideas](www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Capstone Analysis of Your Own Design\r\n * (ideas?)\r\n\r\n\r\n***\r\n### Further Study Resources:\r\n* [Coursera](http://coursera.org)\r\n* [Khan Academy](https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/term-life-insurance-and-death-probability)\r\n* [Wolfram Alpha](http://www.wolframalpha.com/input/?i=torus)\r\n* [Wikipedia](http://en.wikipedia.org/wiki/List_of_cognitive_biases)\r\n* [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Kindle .mobis\r\n* Fave PopSci Read: [The Signal and The Noise](http://www.amazon.com/Signal-Noise-Predictions-Fail-but-ebook/dp/B007V65R54/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=8-1&qid=1376699450) Nate Silver\r\n\r\n## Contribute\r\n\r\nI'm not the expert -- please Share and Contribute. **It's Open Source**!\r\n\r\nTake a look at [my transcript](https://github.com/datasciencemasters/go/wiki/%5BTranscript%5D-Clare-Corthell); Please **showcase your transcript on the [wiki](https://github.com/datasciencemasters/go/wiki) too!**\r\n\r\n[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file diff --git a/stylesheets/normalize.css b/stylesheets/normalize.css new file mode 100644 index 00000000..bc2ba932 --- /dev/null +++ b/stylesheets/normalize.css @@ -0,0 +1,459 @@ +/* normalize.css 2012-02-07T12:37 UTC - http://github.com/necolas/normalize.css */ +/* ============================================================================= + HTML5 display definitions + ========================================================================== */ +/* + * Corrects block display not defined in IE6/7/8/9 & FF3 + */ +article, +aside, +details, +figcaption, +figure, +footer, +header, +hgroup, +nav, +section, +summary { + display: block; +} + +/* + * Corrects inline-block display not defined in IE6/7/8/9 & FF3 + */ +audio, +canvas, +video { + display: inline-block; + *display: inline; + *zoom: 1; +} + +/* + * Prevents modern browsers from displaying 'audio' without controls + */ +audio:not([controls]) { + display: none; +} + +/* + * Addresses styling for 'hidden' attribute not present in IE7/8/9, FF3, S4 + * Known issue: no IE6 support + */ +[hidden] { + display: none; +} + +/* ============================================================================= + Base + ========================================================================== */ +/* + * 1. Corrects text resizing oddly in IE6/7 when body font-size is set using em units + * http://clagnut.com/blog/348/#c790 + * 2. Prevents iOS text size adjust after orientation change, without disabling user zoom + * www.456bereastreet.com/archive/201012/controlling_text_size_in_safari_for_ios_without_disabling_user_zoom/ + */ +html { + font-size: 100%; + /* 1 */ + -webkit-text-size-adjust: 100%; + /* 2 */ + -ms-text-size-adjust: 100%; + /* 2 */ +} + +/* + * Addresses font-family inconsistency between 'textarea' and other form elements. + */ +html, +button, +input, +select, +textarea { + font-family: sans-serif; +} + +/* + * Addresses margins handled incorrectly in IE6/7 + */ +body { + margin: 0; +} + +/* ============================================================================= + Links + ========================================================================== */ +/* + * Addresses outline displayed oddly in Chrome + */ +a:focus { + outline: thin dotted; +} + +/* + * Improves readability when focused and also mouse hovered in all browsers + * people.opera.com/patrickl/experiments/keyboard/test + */ +a:hover, +a:active { + outline: 0; +} + +/* ============================================================================= + Typography + ========================================================================== */ +/* + * Addresses font sizes and margins set differently in IE6/7 + * Addresses font sizes within 'section' and 'article' in FF4+, Chrome, S5 + */ +h1 { + font-size: 2em; + margin: 0.67em 0; +} + +h2 { + font-size: 1.5em; + margin: 0.83em 0; +} + +h3 { + font-size: 1.17em; + margin: 1em 0; +} + +h4 { + font-size: 1em; + margin: 1.33em 0; +} + +h5 { + font-size: 0.83em; + margin: 1.67em 0; +} + +h6 { + font-size: 0.75em; + margin: 2.33em 0; +} + +/* + * Addresses styling not present in IE7/8/9, S5, Chrome + */ +abbr[title] { + border-bottom: 1px dotted; +} + +/* + * Addresses style set to 'bolder' in FF3+, S4/5, Chrome +*/ +b, +strong { + font-weight: bold; +} + +blockquote { + margin: 1em 40px; +} + +/* + * Addresses styling not present in S5, Chrome + */ +dfn { + font-style: italic; +} + +/* + * Addresses styling not present in IE6/7/8/9 + */ +mark { + background: #ff0; + color: #000; +} + +/* + * Addresses margins set differently in IE6/7 + */ +p, +pre { + margin: 1em 0; +} + +/* + * Corrects font family set oddly in IE6, S4/5, Chrome + * en.wikipedia.org/wiki/User:Davidgothberg/Test59 + */ +pre, +code, +kbd, +samp { + font-family: monospace, serif; + _font-family: 'courier new', monospace; + font-size: 1em; +} + +/* + * 1. Addresses CSS quotes not supported in IE6/7 + * 2. Addresses quote property not supported in S4 + */ +/* 1 */ +q { + quotes: none; +} + +/* 2 */ +q:before, +q:after { + content: ''; + content: none; +} + +small { + font-size: 75%; +} + +/* + * Prevents sub and sup affecting line-height in all browsers + * gist.github.com/413930 + */ +sub, +sup { + font-size: 75%; + line-height: 0; + position: relative; + vertical-align: baseline; +} + +sup { + top: -0.5em; +} + +sub { + bottom: -0.25em; +} + +/* ============================================================================= + Lists + ========================================================================== */ +/* + * Addresses margins set differently in IE6/7 + */ +dl, +menu, +ol, +ul { + margin: 1em 0; +} + +dd { + margin: 0 0 0 40px; +} + +/* + * Addresses paddings set differently in IE6/7 + */ +menu, +ol, +ul { + padding: 0 0 0 40px; +} + +/* + * Corrects list images handled incorrectly in IE7 + */ +nav ul, +nav ol { + list-style: none; + list-style-image: none; +} + +/* ============================================================================= + Embedded content + ========================================================================== */ +/* + * 1. Removes border when inside 'a' element in IE6/7/8/9, FF3 + * 2. Improves image quality when scaled in IE7 + * code.flickr.com/blog/2008/11/12/on-ui-quality-the-little-things-client-side-image-resizing/ + */ +img { + border: 0; + /* 1 */ + -ms-interpolation-mode: bicubic; + /* 2 */ +} + +/* + * Corrects overflow displayed oddly in IE9 + */ +svg:not(:root) { + overflow: hidden; +} + +/* ============================================================================= + Figures + ========================================================================== */ +/* + * Addresses margin not present in IE6/7/8/9, S5, O11 + */ +figure { + margin: 0; +} + +/* ============================================================================= + Forms + ========================================================================== */ +/* + * Corrects margin displayed oddly in IE6/7 + */ +form { + margin: 0; +} + +/* + * Define consistent border, margin, and padding + */ +fieldset { + border: 1px solid #c0c0c0; + margin: 0 2px; + padding: 0.35em 0.625em 0.75em; +} + +/* + * 1. Corrects color not being inherited in IE6/7/8/9 + * 2. Corrects text not wrapping in FF3 + * 3. Corrects alignment displayed oddly in IE6/7 + */ +legend { + border: 0; + /* 1 */ + padding: 0; + white-space: normal; + /* 2 */ + *margin-left: -7px; + /* 3 */ +} + +/* + * 1. Corrects font size not being inherited in all browsers + * 2. Addresses margins set differently in IE6/7, FF3+, S5, Chrome + * 3. Improves appearance and consistency in all browsers + */ +button, +input, +select, +textarea { + font-size: 100%; + /* 1 */ + margin: 0; + /* 2 */ + vertical-align: baseline; + /* 3 */ + *vertical-align: middle; + /* 3 */ +} + +/* + * Addresses FF3/4 setting line-height on 'input' using !important in the UA stylesheet + */ +button, +input { + line-height: normal; + /* 1 */ +} + +/* + * 1. Improves usability and consistency of cursor style between image-type 'input' and others + * 2. Corrects inability to style clickable 'input' types in iOS + * 3. Removes inner spacing in IE7 without affecting normal text inputs + * Known issue: inner spacing remains in IE6 + */ +button, +input[type="button"], +input[type="reset"], +input[type="submit"] { + cursor: pointer; + /* 1 */ + -webkit-appearance: button; + /* 2 */ + *overflow: visible; + /* 3 */ +} + +/* + * Re-set default cursor for disabled elements + */ +button[disabled], +input[disabled] { + cursor: default; +} + +/* + * 1. Addresses box sizing set to content-box in IE8/9 + * 2. Removes excess padding in IE8/9 + * 3. Removes excess padding in IE7 + Known issue: excess padding remains in IE6 + */ +input[type="checkbox"], +input[type="radio"] { + box-sizing: border-box; + /* 1 */ + padding: 0; + /* 2 */ + *height: 13px; + /* 3 */ + *width: 13px; + /* 3 */ +} + +/* + * 1. Addresses appearance set to searchfield in S5, Chrome + * 2. Addresses box-sizing set to border-box in S5, Chrome (include -moz to future-proof) + */ +input[type="search"] { + -webkit-appearance: textfield; + /* 1 */ + -moz-box-sizing: content-box; + -webkit-box-sizing: content-box; + /* 2 */ + box-sizing: content-box; +} + +/* + * Removes inner padding and search cancel button in S5, Chrome on OS X + */ +input[type="search"]::-webkit-search-decoration, +input[type="search"]::-webkit-search-cancel-button { + -webkit-appearance: none; +} + +/* + * Removes inner padding and border in FF3+ + * www.sitepen.com/blog/2008/05/14/the-devils-in-the-details-fixing-dojos-toolbar-buttons/ + */ +button::-moz-focus-inner, +input::-moz-focus-inner { + border: 0; + padding: 0; +} + +/* + * 1. Removes default vertical scrollbar in IE6/7/8/9 + * 2. Improves readability and alignment in all browsers + */ +textarea { + overflow: auto; + /* 1 */ + vertical-align: top; + /* 2 */ +} + +/* ============================================================================= + Tables + ========================================================================== */ +/* + * Remove most spacing between table cells + */ +table { + border-collapse: collapse; + border-spacing: 0; +} diff --git a/stylesheets/pygment_trac.css b/stylesheets/pygment_trac.css index c6a6452d..62fd970d 100644 --- a/stylesheets/pygment_trac.css +++ b/stylesheets/pygment_trac.css @@ -1,69 +1,70 @@ -.highlight { background: #ffffff; } -.highlight .c { color: #999988; font-style: italic } /* Comment */ +.highlight .hll { background-color: #404040 } +.highlight { color: #d0d0d0 } +.highlight .c { color: #999999; font-style: italic } /* Comment */ .highlight .err { color: #a61717; background-color: #e3d2d2 } /* Error */ -.highlight .k { font-weight: bold } /* Keyword */ -.highlight .o { font-weight: bold } /* Operator */ -.highlight .cm { color: #999988; 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+/* +Leap Day for GitHub Pages +by Matt Graham +*/ +@font-face { + font-family: 'Quattrocento Sans'; + src: url("../fonts/quattrocentosans-bold-webfont.eot"); + src: url("../fonts/quattrocentosans-bold-webfont.eot?#iefix") format("embedded-opentype"), url("../fonts/quattrocentosans-bold-webfont.woff") format("woff"), url("../fonts/quattrocentosans-bold-webfont.ttf") format("truetype"), url("../fonts/quattrocentosans-bold-webfont.svg#QuattrocentoSansBold") format("svg"); + font-weight: bold; + font-style: normal; +} + +@font-face { + font-family: 'Quattrocento Sans'; + src: url("../fonts/quattrocentosans-bolditalic-webfont.eot"); + src: url("../fonts/quattrocentosans-bolditalic-webfont.eot?#iefix") format("embedded-opentype"), url("../fonts/quattrocentosans-bolditalic-webfont.woff") format("woff"), url("../fonts/quattrocentosans-bolditalic-webfont.ttf") format("truetype"), url("../fonts/quattrocentosans-bolditalic-webfont.svg#QuattrocentoSansBoldItalic") format("svg"); + font-weight: bold; + font-style: italic; +} + +@font-face { + font-family: 'Quattrocento Sans'; + src: url("../fonts/quattrocentosans-italic-webfont.eot"); + src: url("../fonts/quattrocentosans-italic-webfont.eot?#iefix") format("embedded-opentype"), url("../fonts/quattrocentosans-italic-webfont.woff") format("woff"), url("../fonts/quattrocentosans-italic-webfont.ttf") format("truetype"), url("../fonts/quattrocentosans-italic-webfont.svg#QuattrocentoSansItalic") format("svg"); + font-weight: normal; + font-style: italic; +} + +@font-face { + font-family: 'Quattrocento Sans'; + src: url("../fonts/quattrocentosans-regular-webfont.eot"); + src: url("../fonts/quattrocentosans-regular-webfont.eot?#iefix") format("embedded-opentype"), url("../fonts/quattrocentosans-regular-webfont.woff") format("woff"), url("../fonts/quattrocentosans-regular-webfont.ttf") format("truetype"), url("../fonts/quattrocentosans-regular-webfont.svg#QuattrocentoSansRegular") format("svg"); + font-weight: normal; + font-style: normal; +} + +@font-face { + font-family: 'Copse'; + src: url("../fonts/copse-regular-webfont.eot"); + src: url("../fonts/copse-regular-webfont.eot?#iefix") format("embedded-opentype"), url("../fonts/copse-regular-webfont.woff") format("woff"), url("../fonts/copse-regular-webfont.ttf") format("truetype"), url("../fonts/copse-regular-webfont.svg#CopseRegular") format("svg"); + font-weight: normal; + font-style: normal; +} + +/* normalize.css 2012-02-07T12:37 UTC - http://github.com/necolas/normalize.css */ +/* ============================================================================= + HTML5 display definitions + ========================================================================== */ +/* + * Corrects block display not defined in IE6/7/8/9 & FF3 + */ +article, +aside, +details, +figcaption, +figure, +footer, +header, +hgroup, +nav, +section, +summary { + display: block; +} + +/* + * Corrects inline-block display not defined in IE6/7/8/9 & FF3 + */ +audio, +canvas, +video { + display: inline-block; + *display: inline; + *zoom: 1; +} + +/* + * Prevents modern browsers from displaying 'audio' without controls + */ +audio:not([controls]) { + display: none; +} + +/* + * Addresses styling for 'hidden' attribute not present in IE7/8/9, FF3, S4 + * Known issue: no IE6 support + */ +[hidden] { + display: none; +} + +/* ============================================================================= + Base + ========================================================================== */ +/* + * 1. Corrects text resizing oddly in IE6/7 when body font-size is set using em units + * http://clagnut.com/blog/348/#c790 + * 2. Prevents iOS text size adjust after orientation change, without disabling user zoom + * www.456bereastreet.com/archive/201012/controlling_text_size_in_safari_for_ios_without_disabling_user_zoom/ + */ +html { + font-size: 100%; + /* 1 */ + -webkit-text-size-adjust: 100%; + /* 2 */ + -ms-text-size-adjust: 100%; + /* 2 */ +} + +/* + * Addresses font-family inconsistency between 'textarea' and other form elements. + */ +html, +button, +input, +select, +textarea { + font-family: sans-serif; +} + +/* + * Addresses margins handled incorrectly in IE6/7 + */ +body { + margin: 0; +} + +/* ============================================================================= + Links + ========================================================================== */ +/* + * Addresses outline displayed oddly in Chrome + */ +a:focus { + outline: thin dotted; +} + +/* + * Improves readability when focused and also mouse hovered in all browsers + * people.opera.com/patrickl/experiments/keyboard/test + */ +a:hover, +a:active { + outline: 0; +} + +/* ============================================================================= + Typography + ========================================================================== */ +/* + * Addresses font sizes and margins set differently in IE6/7 + * Addresses font sizes within 'section' and 'article' in FF4+, Chrome, S5 + */ +h1 { + font-size: 2em; + margin: 0.67em 0; +} + +h2 { + font-size: 1.5em; + margin: 0.83em 0; +} + +h3 { + font-size: 1.17em; + margin: 1em 0; +} + +h4 { + font-size: 1em; + margin: 1.33em 0; +} + +h5 { + font-size: 0.83em; + margin: 1.67em 0; +} + +h6 { + font-size: 0.75em; + margin: 2.33em 0; +} + +/* + * Addresses styling not present in IE7/8/9, S5, Chrome + */ +abbr[title] { + border-bottom: 1px dotted; +} + +/* + * Addresses style set to 'bolder' in FF3+, S4/5, Chrome +*/ +b, +strong { + font-weight: bold; +} + +blockquote { + margin: 1em 40px; +} + +/* + * Addresses styling not present in S5, Chrome + */ +dfn { + font-style: italic; +} + +/* + * Addresses styling not present in IE6/7/8/9 + */ +mark { + background: #ff0; + color: #000; +} + +/* + * Addresses margins set differently in IE6/7 + */ +p, +pre { + margin: 1em 0; +} + +/* + * Corrects font family set oddly in IE6, S4/5, Chrome + * en.wikipedia.org/wiki/User:Davidgothberg/Test59 + */ +pre, +code, +kbd, +samp { + font-family: monospace, serif; + _font-family: 'courier new', monospace; + font-size: 1em; +} + +/* + * 1. Addresses CSS quotes not supported in IE6/7 + * 2. Addresses quote property not supported in S4 + */ +/* 1 */ +q { + quotes: none; +} + +/* 2 */ +q:before, +q:after { + content: ''; + content: none; +} + +small { + font-size: 75%; +} + +/* + * Prevents sub and sup affecting line-height in all browsers + * gist.github.com/413930 + */ +sub, +sup { + font-size: 75%; + line-height: 0; + position: relative; + vertical-align: baseline; +} + +sup { + top: -0.5em; +} + +sub { + bottom: -0.25em; +} + +/* ============================================================================= + Lists + ========================================================================== */ +/* + * Addresses margins set differently in IE6/7 + */ +dl, +menu, +ol, +ul { + margin: 1em 0; +} + +dd { + margin: 0 0 0 40px; +} + +/* + * Addresses paddings set differently in IE6/7 + */ +menu, +ol, +ul { + padding: 0 0 0 40px; +} + +/* + * Corrects list images handled incorrectly in IE7 + */ +nav ul, +nav ol { + list-style: none; + list-style-image: none; +} + +/* ============================================================================= + Embedded content + ========================================================================== */ +/* + * 1. Removes border when inside 'a' element in IE6/7/8/9, FF3 + * 2. Improves image quality when scaled in IE7 + * code.flickr.com/blog/2008/11/12/on-ui-quality-the-little-things-client-side-image-resizing/ + */ +img { + border: 0; + /* 1 */ + -ms-interpolation-mode: bicubic; + /* 2 */ +} + +/* + * Corrects overflow displayed oddly in IE9 + */ +svg:not(:root) { + overflow: hidden; +} + +/* ============================================================================= + Figures + ========================================================================== */ +/* + * Addresses margin not present in IE6/7/8/9, S5, O11 + */ +figure { + margin: 0; +} + +/* ============================================================================= + Forms + ========================================================================== */ +/* + * Corrects margin displayed oddly in IE6/7 + */ +form { + margin: 0; +} + +/* + * Define consistent border, margin, and padding + */ +fieldset { + border: 1px solid #c0c0c0; + margin: 0 2px; + padding: 0.35em 0.625em 0.75em; +} + +/* + * 1. Corrects color not being inherited in IE6/7/8/9 + * 2. Corrects text not wrapping in FF3 + * 3. Corrects alignment displayed oddly in IE6/7 + */ +legend { + border: 0; + /* 1 */ + padding: 0; + white-space: normal; + /* 2 */ + *margin-left: -7px; + /* 3 */ +} + +/* + * 1. Corrects font size not being inherited in all browsers + * 2. Addresses margins set differently in IE6/7, FF3+, S5, Chrome + * 3. Improves appearance and consistency in all browsers + */ +button, +input, +select, +textarea { + font-size: 100%; + /* 1 */ + margin: 0; + /* 2 */ + vertical-align: baseline; + /* 3 */ + *vertical-align: middle; + /* 3 */ +} + +/* + * Addresses FF3/4 setting line-height on 'input' using !important in the UA stylesheet + */ +button, +input { + line-height: normal; + /* 1 */ +} + +/* + * 1. Improves usability and consistency of cursor style between image-type 'input' and others + * 2. Corrects inability to style clickable 'input' types in iOS + * 3. Removes inner spacing in IE7 without affecting normal text inputs + * Known issue: inner spacing remains in IE6 + */ +button, +input[type="button"], +input[type="reset"], +input[type="submit"] { + cursor: pointer; + /* 1 */ + -webkit-appearance: button; + /* 2 */ + *overflow: visible; + /* 3 */ +} + +/* + * Re-set default cursor for disabled elements + */ +button[disabled], +input[disabled] { + cursor: default; +} + +/* + * 1. Addresses box sizing set to content-box in IE8/9 + * 2. Removes excess padding in IE8/9 + * 3. Removes excess padding in IE7 + Known issue: excess padding remains in IE6 + */ +input[type="checkbox"], +input[type="radio"] { + box-sizing: border-box; + /* 1 */ + padding: 0; + /* 2 */ + *height: 13px; + /* 3 */ + *width: 13px; + /* 3 */ +} + +/* + * 1. Addresses appearance set to searchfield in S5, Chrome + * 2. Addresses box-sizing set to border-box in S5, Chrome (include -moz to future-proof) + */ +input[type="search"] { + -webkit-appearance: textfield; + /* 1 */ + -moz-box-sizing: content-box; + -webkit-box-sizing: content-box; + /* 2 */ + box-sizing: content-box; +} + +/* + * Removes inner padding and search cancel button in S5, Chrome on OS X + */ +input[type="search"]::-webkit-search-decoration, +input[type="search"]::-webkit-search-cancel-button { + -webkit-appearance: none; +} + +/* + * Removes inner padding and border in FF3+ + * www.sitepen.com/blog/2008/05/14/the-devils-in-the-details-fixing-dojos-toolbar-buttons/ + */ +button::-moz-focus-inner, +input::-moz-focus-inner { + border: 0; + padding: 0; +} + +/* + * 1. Removes default vertical scrollbar in IE6/7/8/9 + * 2. Improves readability and alignment in all browsers + */ +textarea { + overflow: auto; + /* 1 */ + vertical-align: top; + /* 2 */ +} + +/* ============================================================================= + Tables + ========================================================================== */ +/* + * Remove most spacing between table cells + */ +table { + border-collapse: collapse; + border-spacing: 0; +} body { - padding:50px; - font:14px/1.5 Lato, "Helvetica Neue", Helvetica, Arial, sans-serif; - color:#777; - font-weight:300; + font: 14px/22px "Quattrocento Sans", "Helvetica Neue", Helvetica, Arial, sans-serif; + color: #666; + font-weight: 300; + margin: 0px; + padding: 0px 0 20px 0px; + background: url(../images/body-background.png) #eae6d1; } h1, h2, h3, h4, h5, h6 { - color:#222; - margin:0 0 20px; + color: #333; + margin: 0 0 10px; } p, ul, ol, table, pre, dl { - margin:0 0 20px; + margin: 0 0 20px; } h1, h2, h3 { - line-height:1.1; + line-height: 1.1; } h1 { - font-size:28px; + font-size: 28px; } h2 { - color:#393939; + font-size: 24px; + color: #393939; } h3, h4, h5, h6 { - color:#494949; + color: #666666; +} + +h3 { + font-size: 18px; + line-height: 24px; } a { - color:#39c; - font-weight:400; - text-decoration:none; + color: #3399cc; + font-weight: 400; + text-decoration: none; } a small { - font-size:11px; - color:#777; - margin-top:-0.6em; - display:block; + font-size: 11px; + color: #666; + margin-top: -0.6em; + display: block; } -.wrapper { - width:860px; - margin:0 auto; +ul { + list-style-image: url("../images/bullet.png"); } -blockquote { - border-left:1px solid #e5e5e5; - margin:0; - padding:0 0 0 20px; - font-style:italic; +strong { + font-weight: bold; + color: #333; } -code, pre { - font-family:Monaco, Bitstream Vera Sans Mono, Lucida Console, Terminal; - color:#333; - font-size:12px; +.wrapper { + width: 650px; + margin: 0 auto; + position: relative; } -pre { - padding:8px 15px; - background: #f8f8f8; - border-radius:5px; - border:1px solid #e5e5e5; - overflow-x: auto; +section img { + max-width: 100%; } -table { - width:100%; - border-collapse:collapse; +blockquote { + border-left: 1px solid #ffcc00; + margin: 0; + padding: 0 0 0 20px; + font-style: italic; +} + +code { + font-family: "Lucida Sans", Monaco, Bitstream Vera Sans Mono, Lucida Console, Terminal; + font-size: 13px; + color: #efefef; + text-shadow: 0px 1px 0px #000; + margin: 0 4px; + padding: 2px 6px; + background: #333; + -moz-border-radius: 2px; + -webkit-border-radius: 2px; + -o-border-radius: 2px; + -ms-border-radius: 2px; + -khtml-border-radius: 2px; + border-radius: 2px; } -th, td { - text-align:left; - padding:5px 10px; - border-bottom:1px solid #e5e5e5; +pre { + padding: 8px 15px; + background: #333333; + -moz-border-radius: 3px; + -webkit-border-radius: 3px; + -o-border-radius: 3px; + -ms-border-radius: 3px; + -khtml-border-radius: 3px; + border-radius: 3px; + border: 1px solid #c7c7c7; + overflow: auto; + overflow-y: hidden; +} +pre code { + margin: 0px; + padding: 0px; } -dt { - color:#444; - font-weight:700; +table { + width: 100%; + border-collapse: collapse; } th { - color:#444; -} - -img { - max-width:100%; + text-align: left; + padding: 5px 10px; + border-bottom: 1px solid #e5e5e5; + color: #444; } -header { - width:270px; - float:left; - position:fixed; -} - -header ul { - list-style:none; - height:40px; - - padding:0; - - background: #eee; - background: -moz-linear-gradient(top, #f8f8f8 0%, #dddddd 100%); - background: -webkit-gradient(linear, left top, left bottom, color-stop(0%,#f8f8f8), color-stop(100%,#dddddd)); - background: -webkit-linear-gradient(top, #f8f8f8 0%,#dddddd 100%); - background: -o-linear-gradient(top, #f8f8f8 0%,#dddddd 100%); - background: -ms-linear-gradient(top, #f8f8f8 0%,#dddddd 100%); - background: linear-gradient(top, #f8f8f8 0%,#dddddd 100%); - - border-radius:5px; - border:1px solid #d2d2d2; - box-shadow:inset #fff 0 1px 0, inset rgba(0,0,0,0.03) 0 -1px 0; - width:270px; -} - -header li { - width:89px; - float:left; - border-right:1px solid #d2d2d2; - height:40px; -} - -header ul a { - line-height:1; - font-size:11px; - color:#999; - display:block; - text-align:center; - padding-top:6px; - height:40px; +td { + text-align: left; + padding: 5px 10px; + border-bottom: 1px solid #e5e5e5; + border-right: 1px solid #ffcc00; } - -strong { - color:#222; - font-weight:700; +td:first-child { + border-left: 1px solid #ffcc00; } -header ul li + li { - width:88px; - border-left:1px solid #fff; +hr { + border: 0; + outline: none; + height: 11px; + background: transparent url("../images/hr.gif") center center repeat-x; + margin: 0 0 20px; } -header ul li + li + li { - border-right:none; - width:89px; +dt { + color: #444; + font-weight: 700; } -header ul a strong { - font-size:14px; - display:block; - color:#222; +header { + padding: 25px 20px 40px 20px; + margin: 0; + position: fixed; + top: 0; + left: 0; + right: 0; + width: 100%; + text-align: center; + background: url(../images/background.png) #4276b6; + -moz-box-shadow: 1px 0px 2px rgba(0, 0, 0, 0.75); + -webkit-box-shadow: 1px 0px 2px rgba(0, 0, 0, 0.75); + -o-box-shadow: 1px 0px 2px rgba(0, 0, 0, 0.75); + box-shadow: 1px 0px 2px rgba(0, 0, 0, 0.75); + z-index: 99; + -webkit-font-smoothing: antialiased; + min-height: 76px; +} +header h1 { + font: 40px/48px "Copse", "Helvetica Neue", Helvetica, Arial, sans-serif; + color: #f3f3f3; + text-shadow: 0px 2px 0px #235796; + margin: 0px; + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; + -o-text-overflow: ellipsis; + -ms-text-overflow: ellipsis; +} +header p { + color: #d8d8d8; + text-shadow: rgba(0, 0, 0, 0.2) 0 1px 0; + font-size: 18px; + margin: 0px; +} + +#banner { + z-index: 100; + left: 0; + right: 50%; + height: 50px; + margin-right: -382px; + position: fixed; + top: 115px; + background: #ffcc00; + border: 1px solid #f0b500; + -moz-box-shadow: 0px 1px 3px rgba(0, 0, 0, 0.25); + -webkit-box-shadow: 0px 1px 3px rgba(0, 0, 0, 0.25); + -o-box-shadow: 0px 1px 3px rgba(0, 0, 0, 0.25); + box-shadow: 0px 1px 3px rgba(0, 0, 0, 0.25); + -moz-border-radius: 0px 2px 2px 0px; + -webkit-border-radius: 0px 2px 2px 0px; + -o-border-radius: 0px 2px 2px 0px; + -ms-border-radius: 0px 2px 2px 0px; + -khtml-border-radius: 0px 2px 2px 0px; + border-radius: 0px 2px 2px 0px; + padding-right: 10px; +} +#banner .button { + border: 1px solid #dba500; + background: -webkit-gradient(linear, 50% 0%, 50% 100%, color-stop(0%, #ffe788), color-stop(100%, #ffce38)); + background: -webkit-linear-gradient(#ffe788, #ffce38); + background: -moz-linear-gradient(#ffe788, #ffce38); + background: -o-linear-gradient(#ffe788, #ffce38); + background: -ms-linear-gradient(#ffe788, #ffce38); + background: linear-gradient(#ffe788, #ffce38); + -moz-border-radius: 2px; + -webkit-border-radius: 2px; + -o-border-radius: 2px; + -ms-border-radius: 2px; + -khtml-border-radius: 2px; + border-radius: 2px; + -moz-box-shadow: inset 0px 1px 0px rgba(255, 255, 255, 0.4), 0px 1px 1px rgba(0, 0, 0, 0.1); + -webkit-box-shadow: inset 0px 1px 0px rgba(255, 255, 255, 0.4), 0px 1px 1px rgba(0, 0, 0, 0.1); + -o-box-shadow: inset 0px 1px 0px rgba(255, 255, 255, 0.4), 0px 1px 1px rgba(0, 0, 0, 0.1); + box-shadow: inset 0px 1px 0px rgba(255, 255, 255, 0.4), 0px 1px 1px rgba(0, 0, 0, 0.1); + background-color: #FFE788; + margin-left: 5px; + padding: 10px 12px; + margin-top: 6px; + line-height: 14px; + font-size: 14px; + color: #333; + font-weight: bold; + display: inline-block; + text-align: center; +} +#banner .button:hover { + background: -webkit-gradient(linear, 50% 0%, 50% 100%, color-stop(0%, #ffe788), color-stop(100%, #ffe788)); + background: -webkit-linear-gradient(#ffe788, #ffe788); + background: -moz-linear-gradient(#ffe788, #ffe788); + background: -o-linear-gradient(#ffe788, #ffe788); + background: -ms-linear-gradient(#ffe788, #ffe788); + background: linear-gradient(#ffe788, #ffe788); + background-color: #ffeca0; +} +#banner .fork { + position: fixed; + left: 50%; + margin-left: -325px; + padding: 10px 12px; + margin-top: 6px; + line-height: 14px; + font-size: 14px; + background-color: #FFE788; +} +#banner .downloads { + float: right; + margin: 0 45px 0 0; +} +#banner .downloads span { + float: left; + line-height: 52px; + font-size: 90%; + color: #9d7f0d; + text-transform: uppercase; + text-shadow: rgba(255, 255, 255, 0.2) 0 1px 0; +} +#banner ul { + list-style: none; + height: 40px; + padding: 0; + float: left; + margin-left: 10px; +} +#banner ul li { + display: inline; +} +#banner ul li a.button { + background-color: #FFE788; +} +#banner #logo { + position: absolute; + height: 36px; + width: 36px; + right: 7px; + top: 7px; + display: block; + background: url(../images/octocat-logo.png); } section { - width:500px; - float:right; - padding-bottom:50px; + width: 590px; + padding: 30px 30px 50px 30px; + margin: 20px 0; + margin-top: 190px; + position: relative; + background: #fbfbfb; + -moz-border-radius: 3px; + -webkit-border-radius: 3px; + -o-border-radius: 3px; + -ms-border-radius: 3px; + -khtml-border-radius: 3px; + border-radius: 3px; + border: 1px solid #cbcbcb; + -moz-box-shadow: 0px 1px 2px rgba(0, 0, 0, 0.09), inset 0px 0px 2px 2px rgba(255, 255, 255, 0.5), inset 0 0 5px 5px rgba(255, 255, 255, 0.4); + -webkit-box-shadow: 0px 1px 2px rgba(0, 0, 0, 0.09), inset 0px 0px 2px 2px rgba(255, 255, 255, 0.5), inset 0 0 5px 5px rgba(255, 255, 255, 0.4); + -o-box-shadow: 0px 1px 2px rgba(0, 0, 0, 0.09), inset 0px 0px 2px 2px rgba(255, 255, 255, 0.5), inset 0 0 5px 5px rgba(255, 255, 255, 0.4); + box-shadow: 0px 1px 2px rgba(0, 0, 0, 0.09), inset 0px 0px 2px 2px rgba(255, 255, 255, 0.5), inset 0 0 5px 5px rgba(255, 255, 255, 0.4); } small { - font-size:11px; + font-size: 12px; } -hr { - border:0; - background:#e5e5e5; - height:1px; - margin:0 0 20px; +nav { + width: 230px; + position: fixed; + top: 220px; + left: 50%; + margin-left: -580px; + text-align: right; +} +nav ul { + list-style: none; + list-style-image: none; + font-size: 14px; + line-height: 24px; +} +nav ul li { + padding: 5px 0px; + line-height: 16px; +} +nav ul li.tag-h1 { + font-size: 1.2em; +} +nav ul li.tag-h1 a { + font-weight: bold; + color: #333; +} +nav ul li.tag-h2 + .tag-h1 { + margin-top: 10px; +} +nav ul a { + color: #666; +} +nav ul a:hover { + color: #999; } footer { - width:270px; - float:left; - position:fixed; - bottom:50px; + width: 180px; + position: fixed; + left: 50%; + margin-left: -530px; + bottom: 20px; + text-align: right; + line-height: 16px; } -@media print, screen and (max-width: 960px) { - +@media print, screen and (max-width: 1060px) { div.wrapper { - width:auto; - margin:0; + width: auto; + margin: 0; + } + + nav { + display: none; } - + header, section, footer { - float:none; - position:static; - width:auto; + float: none; } - - header { - padding-right:320px; + header h1, section h1, footer h1 { + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; + -o-text-overflow: ellipsis; + -ms-text-overflow: ellipsis; } - - section { - border:1px solid #e5e5e5; - border-width:1px 0; - padding:20px 0; - margin:0 0 20px; + + #banner { + width: 100%; } - - header a small { - display:inline; + #banner .downloads { + margin-right: 60px; } - - header ul { - position:absolute; - right:50px; - top:52px; + #banner #logo { + margin-right: 15px; } -} + section { + border: 1px solid #e5e5e5; + border-width: 1px 0; + padding: 20px auto; + margin: 190px auto 20px; + max-width: 600px; + } + + footer { + text-align: center; + margin: 20px auto; + position: relative; + left: auto; + bottom: auto; + width: auto; + } +} @media print, screen and (max-width: 720px) { body { - word-wrap:break-word; + word-wrap: break-word; } - + header { - padding:0; + padding: 20px 20px; + margin: 0; } - - header ul, header p.view { - position:static; + header h1 { + font-size: 32px; + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; + -o-text-overflow: ellipsis; + -ms-text-overflow: ellipsis; } - - pre, code { - word-wrap:normal; + header p { + display: none; + } + + #banner { + top: 80px; + } + #banner .fork { + float: left; + display: inline-block; + margin-left: 0px; + position: fixed; + left: 20px; + } + + section { + margin-top: 130px; + margin-bottom: 0px; + width: auto; } -} + header ul, header p.view { + position: static; + } +} @media print, screen and (max-width: 480px) { - body { - padding:15px; + header { + position: relative; + padding: 5px 0px; + min-height: 0px; + } + header h1 { + font-size: 24px; + white-space: nowrap; + overflow: hidden; + text-overflow: ellipsis; + -o-text-overflow: ellipsis; + -ms-text-overflow: ellipsis; + } + + section { + margin-top: 5px; + } + + #banner { + display: none; } - + header ul { - display:none; + display: none; } } - @media print { body { - padding:0.4in; - font-size:12pt; - color:#444; + padding: 0.4in; + font-size: 12pt; + color: #444; + } +} +@media print, screen and (max-height: 680px) { + footer { + text-align: center; + margin: 20px auto; + position: relative; + left: auto; + bottom: auto; + width: auto; + } +} +@media print, screen and (max-height: 480px) { + nav { + display: none; + } + + footer { + text-align: center; + margin: 20px auto; + position: relative; + left: auto; + bottom: auto; + width: auto; } } From b25971bafb088de2768483e6e4c9494991f97635 Mon Sep 17 00:00:00 2001 From: Clare Date: Fri, 16 Aug 2013 21:55:08 -0700 Subject: [PATCH 12/35] Create gh-pages branch via GitHub --- index.html | 2 ++ params.json | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/index.html b/index.html index b2080143..e6c517bf 100644 --- a/index.html +++ b/index.html @@ -41,6 +41,8 @@

    The /Open Source/ Data Science Masters

    The Curriculum for learning Data Science, Open Source and at your fingertips.

    +

    NB: Wikis are easier to edit, so the community version is found HERE.

    +

    The Internet is Your Oyster

    diff --git a/params.json b/params.json index 1289e231..3a042a0f 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"The /Open Source/ Data Science Masters","tagline":"Curriculum","body":"The Curriculum for learning Data Science, Open Source and at your fingertips.\r\n\r\n## The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*See [My Curriculum](http://bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n## The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n## An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n**Ready?**\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\n* **Intro to Data Science** [UW / Coursera](https://www.coursera.org/course/datasci)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n### Math\r\n* Linear Algebra / Levandosky [Stanford / Book](http://www.amazon.com/Linear-Algebra-Steven-Levandosky/dp/0536667470/ref=sr_1_1?ie=UTF8&qid=1376546498&sr=8-1&keywords=linear+algebra+levandosky#)\r\n* Statistics [Stats in a Nutshell / Book](http://shop.oreilly.com/product/9780596510497.do)\r\n* Problem-Solving Heuristics \"How To Solve It\" [Polya / Book](http://en.wikipedia.org/wiki/How_to_Solve_It)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](https://www.coursera.org/course/algo)\r\n * Algorithm Design [Kleinberg & Tardos / Book](http://www.amazon.com/Algorithm-Design-Jon-Kleinberg/dp/0321295358/ref=sr_1_1?ie=UTF8&qid=1376702127&sr=8-1&keywords=kleinberg+algorithms)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Coursera](https://www.coursera.org/course/db)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Book](http://i.stanford.edu/~ullman/mmds.html)\r\n * Mining The Social Web [O'Reilly / Book](http://shop.oreilly.com/product/0636920010203.do)\r\n * Introduction to Information Retrieval [Stanford / Book](http://nlp.stanford.edu/IR-book/information-retrieval-book.html)\r\n\r\n* **Machine Learning**\r\n * Machine Learning / Ng [Stanford / Coursera](https://www.coursera.org/course/ml)\r\n * Programming Collective Intelligence [O'Reilly / Book]()\r\n * Statistics [The Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) #current\r\n\r\n* **Probabilistic Graphical Models**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)\r\n * PGMs / Koller [Stanford / Coursera](https://www.coursera.org/course/pgm)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python [O'Reilly / Book](http://shop.oreilly.com/product/9780596516499.do)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [O'Reilly / Book](http://www.kqzyfj.com/click-7040302-11260198?url=http%3A%2F%2Fshop.oreilly.com%2Fproduct%2F0636920023784.do&cjsku=0636920023784)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://blogs.ischool.berkeley.edu/i290-abdt-s12/)\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](https://www.coursera.org/course/networksonline)\r\n * Information Visualization [\"Envisioning Information\" Tufte / Book](http://www.amazon.com/Envisioning-Information-Edward-R-Tufte/dp/0961392118/ref=sr_1_8?ie=UTF8&qid=1376709039&sr=8-8&keywords=information+design)\r\n\r\n* **Python** (Learning)\r\n * New To Python: [Learn Python the Hard Way](http://learnpythonthehardway.org/), [Google's Python Class](code.google.com/edu/languages/google-python-class/)\r\n\r\n* Python (Libraries)\r\n * Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://www.lowindata.com/2013/installing-scientific-python-on-mac-os-x/)\r\n * Bayesian Inference | [pymc](https://github.com/pymc-devs/pymc)\r\n * Labeled data structures objects, statistical functions, etc [pandas](https://github.com/pydata/pandas) (See: Python for Data Analysis)\r\n * Python wrapper for the Twitter API [twython](https://github.com/ryanmcgrath/twython)\r\n * Tools for Data Mining & Analysis [scikit-learn](http://scikit-learn.org/stable/)\r\n * Network Modeling & Viz [networkx](http://networkx.github.io/)\r\n * Natural Language Toolkit [NLTK](http://nltk.org/)\r\n\r\n### Capstone Project\r\n* [Toy Data Ideas](www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Capstone Analysis of Your Own Design\r\n * (ideas?)\r\n\r\n\r\n***\r\n### Further Study Resources:\r\n* [Coursera](http://coursera.org)\r\n* [Khan Academy](https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/term-life-insurance-and-death-probability)\r\n* [Wolfram Alpha](http://www.wolframalpha.com/input/?i=torus)\r\n* [Wikipedia](http://en.wikipedia.org/wiki/List_of_cognitive_biases)\r\n* [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Kindle .mobis\r\n* Fave PopSci Read: [The Signal and The Noise](http://www.amazon.com/Signal-Noise-Predictions-Fail-but-ebook/dp/B007V65R54/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=8-1&qid=1376699450) Nate Silver\r\n\r\n## Contribute\r\n\r\nI'm not the expert -- please Share and Contribute. **It's Open Source**!\r\n\r\nTake a look at [my transcript](https://github.com/datasciencemasters/go/wiki/%5BTranscript%5D-Clare-Corthell); Please **showcase your transcript on the [wiki](https://github.com/datasciencemasters/go/wiki) too!**\r\n\r\n[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The /Open Source/ Data Science Masters","tagline":"Curriculum","body":"The Curriculum for learning Data Science, Open Source and at your fingertips.\r\n\r\n*NB: Wikis are easier to edit, so the community version is found [HERE](https://github.com/datasciencemasters/go/wiki).*\r\n\r\n## The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*See [My Curriculum](http://bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n## The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n## An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n**Ready?**\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\n* **Intro to Data Science** [UW / Coursera](https://www.coursera.org/course/datasci)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n### Math\r\n* Linear Algebra / Levandosky [Stanford / Book](http://www.amazon.com/Linear-Algebra-Steven-Levandosky/dp/0536667470/ref=sr_1_1?ie=UTF8&qid=1376546498&sr=8-1&keywords=linear+algebra+levandosky#)\r\n* Statistics [Stats in a Nutshell / Book](http://shop.oreilly.com/product/9780596510497.do)\r\n* Problem-Solving Heuristics \"How To Solve It\" [Polya / Book](http://en.wikipedia.org/wiki/How_to_Solve_It)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](https://www.coursera.org/course/algo)\r\n * Algorithm Design [Kleinberg & Tardos / Book](http://www.amazon.com/Algorithm-Design-Jon-Kleinberg/dp/0321295358/ref=sr_1_1?ie=UTF8&qid=1376702127&sr=8-1&keywords=kleinberg+algorithms)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Coursera](https://www.coursera.org/course/db)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Book](http://i.stanford.edu/~ullman/mmds.html)\r\n * Mining The Social Web [O'Reilly / Book](http://shop.oreilly.com/product/0636920010203.do)\r\n * Introduction to Information Retrieval [Stanford / Book](http://nlp.stanford.edu/IR-book/information-retrieval-book.html)\r\n\r\n* **Machine Learning**\r\n * Machine Learning / Ng [Stanford / Coursera](https://www.coursera.org/course/ml)\r\n * Programming Collective Intelligence [O'Reilly / Book]()\r\n * Statistics [The Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) #current\r\n\r\n* **Probabilistic Graphical Models**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)\r\n * PGMs / Koller [Stanford / Coursera](https://www.coursera.org/course/pgm)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python [O'Reilly / Book](http://shop.oreilly.com/product/9780596516499.do)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [O'Reilly / Book](http://www.kqzyfj.com/click-7040302-11260198?url=http%3A%2F%2Fshop.oreilly.com%2Fproduct%2F0636920023784.do&cjsku=0636920023784)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://blogs.ischool.berkeley.edu/i290-abdt-s12/)\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](https://www.coursera.org/course/networksonline)\r\n * Information Visualization [\"Envisioning Information\" Tufte / Book](http://www.amazon.com/Envisioning-Information-Edward-R-Tufte/dp/0961392118/ref=sr_1_8?ie=UTF8&qid=1376709039&sr=8-8&keywords=information+design)\r\n\r\n* **Python** (Learning)\r\n * New To Python: [Learn Python the Hard Way](http://learnpythonthehardway.org/), [Google's Python Class](code.google.com/edu/languages/google-python-class/)\r\n\r\n* Python (Libraries)\r\n * Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://www.lowindata.com/2013/installing-scientific-python-on-mac-os-x/)\r\n * Bayesian Inference | [pymc](https://github.com/pymc-devs/pymc)\r\n * Labeled data structures objects, statistical functions, etc [pandas](https://github.com/pydata/pandas) (See: Python for Data Analysis)\r\n * Python wrapper for the Twitter API [twython](https://github.com/ryanmcgrath/twython)\r\n * Tools for Data Mining & Analysis [scikit-learn](http://scikit-learn.org/stable/)\r\n * Network Modeling & Viz [networkx](http://networkx.github.io/)\r\n * Natural Language Toolkit [NLTK](http://nltk.org/)\r\n\r\n### Capstone Project\r\n* [Toy Data Ideas](www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Capstone Analysis of Your Own Design\r\n * (ideas?)\r\n\r\n\r\n***\r\n### Further Study Resources:\r\n* [Coursera](http://coursera.org)\r\n* [Khan Academy](https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/term-life-insurance-and-death-probability)\r\n* [Wolfram Alpha](http://www.wolframalpha.com/input/?i=torus)\r\n* [Wikipedia](http://en.wikipedia.org/wiki/List_of_cognitive_biases)\r\n* [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Kindle .mobis\r\n* Fave PopSci Read: [The Signal and The Noise](http://www.amazon.com/Signal-Noise-Predictions-Fail-but-ebook/dp/B007V65R54/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=8-1&qid=1376699450) Nate Silver\r\n\r\n## Contribute\r\n\r\nI'm not the expert -- please Share and Contribute. **It's Open Source**!\r\n\r\nTake a look at [my transcript](https://github.com/datasciencemasters/go/wiki/%5BTranscript%5D-Clare-Corthell); Please **showcase your transcript on the [wiki](https://github.com/datasciencemasters/go/wiki) too!**\r\n\r\n[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file From f2468678489dc46af04750cda3dc58426b8a7b7a Mon Sep 17 00:00:00 2001 From: Clare Date: Tue, 3 Sep 2013 11:18:23 -0700 Subject: [PATCH 13/35] Create gh-pages branch via GitHub --- index.html | 44 ++++++++++++++++++++++++++++++-------------- params.json | 2 +- 2 files changed, 31 insertions(+), 15 deletions(-) diff --git a/index.html b/index.html index e6c517bf..0f4692ae 100644 --- a/index.html +++ b/index.html @@ -3,7 +3,7 @@ - The /Open Source/ Data Science Masters by datasciencemasters + The Open Source Data Science Masters by datasciencemasters @@ -17,7 +17,7 @@
    -

    The /Open Source/ Data Science Masters

    +

    The Open Source Data Science Masters

    Curriculum

    @@ -41,14 +41,14 @@

    The /Open Source/ Data Science Masters

    The Curriculum for learning Data Science, Open Source and at your fingertips.

    -

    NB: Wikis are easier to edit, so the community version is found HERE.

    +

    NB: Wikis are easier to edit, so the **community version* is found HERE.*

    The Internet is Your Oyster

    I didn't want to wait. I wanted to work on things I care about now. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?

    -

    See My Curriculum

    +

    See My Forked Transcript

    With Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?

    @@ -63,6 +63,12 @@

    -- McKinsey Report Highlights the Impending Data Scientist Shortage 23 July 2013

    +
    +

    There are little to no Data Scientists with 5 years experience, because the job simply did not exist.

    +
    + +

    -- David Hardtke How To Hire A Data Scientist 13 Nov 2012

    +

    An Academic Shortfall

    @@ -81,7 +87,9 @@

    Ready?

    -The Open Source Data Science Curriculum

    +The How: An Open Source Data Science Curriculum

    + +

    Start here.

    • @@ -188,7 +196,7 @@

  • -

    Python (Libraries)

    +

    Python (Libraries)

    Contribute

    I'm not the expert -- please Share and Contribute. It's Open Source!

    -

    Take a look at my transcript; Please showcase your transcript on the wiki too!

    +

    Here's my transcript; Please showcase your own on the wiki!

    Follow me on Twitter @clarecorthell

    • @@ -243,6 +249,16 @@

    - + + + \ No newline at end of file diff --git a/params.json b/params.json index 3a042a0f..91d09e7d 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"The /Open Source/ Data Science Masters","tagline":"Curriculum","body":"The Curriculum for learning Data Science, Open Source and at your fingertips.\r\n\r\n*NB: Wikis are easier to edit, so the community version is found [HERE](https://github.com/datasciencemasters/go/wiki).*\r\n\r\n## The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*See [My Curriculum](http://bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n## The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n## An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n**Ready?**\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\n* **Intro to Data Science** [UW / Coursera](https://www.coursera.org/course/datasci)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n### Math\r\n* Linear Algebra / Levandosky [Stanford / Book](http://www.amazon.com/Linear-Algebra-Steven-Levandosky/dp/0536667470/ref=sr_1_1?ie=UTF8&qid=1376546498&sr=8-1&keywords=linear+algebra+levandosky#)\r\n* Statistics [Stats in a Nutshell / Book](http://shop.oreilly.com/product/9780596510497.do)\r\n* Problem-Solving Heuristics \"How To Solve It\" [Polya / Book](http://en.wikipedia.org/wiki/How_to_Solve_It)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](https://www.coursera.org/course/algo)\r\n * Algorithm Design [Kleinberg & Tardos / Book](http://www.amazon.com/Algorithm-Design-Jon-Kleinberg/dp/0321295358/ref=sr_1_1?ie=UTF8&qid=1376702127&sr=8-1&keywords=kleinberg+algorithms)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Coursera](https://www.coursera.org/course/db)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Book](http://i.stanford.edu/~ullman/mmds.html)\r\n * Mining The Social Web [O'Reilly / Book](http://shop.oreilly.com/product/0636920010203.do)\r\n * Introduction to Information Retrieval [Stanford / Book](http://nlp.stanford.edu/IR-book/information-retrieval-book.html)\r\n\r\n* **Machine Learning**\r\n * Machine Learning / Ng [Stanford / Coursera](https://www.coursera.org/course/ml)\r\n * Programming Collective Intelligence [O'Reilly / Book]()\r\n * Statistics [The Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) #current\r\n\r\n* **Probabilistic Graphical Models**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)\r\n * PGMs / Koller [Stanford / Coursera](https://www.coursera.org/course/pgm)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python [O'Reilly / Book](http://shop.oreilly.com/product/9780596516499.do)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [O'Reilly / Book](http://www.kqzyfj.com/click-7040302-11260198?url=http%3A%2F%2Fshop.oreilly.com%2Fproduct%2F0636920023784.do&cjsku=0636920023784)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://blogs.ischool.berkeley.edu/i290-abdt-s12/)\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](https://www.coursera.org/course/networksonline)\r\n * Information Visualization [\"Envisioning Information\" Tufte / Book](http://www.amazon.com/Envisioning-Information-Edward-R-Tufte/dp/0961392118/ref=sr_1_8?ie=UTF8&qid=1376709039&sr=8-8&keywords=information+design)\r\n\r\n* **Python** (Learning)\r\n * New To Python: [Learn Python the Hard Way](http://learnpythonthehardway.org/), [Google's Python Class](code.google.com/edu/languages/google-python-class/)\r\n\r\n* Python (Libraries)\r\n * Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://www.lowindata.com/2013/installing-scientific-python-on-mac-os-x/)\r\n * Bayesian Inference | [pymc](https://github.com/pymc-devs/pymc)\r\n * Labeled data structures objects, statistical functions, etc [pandas](https://github.com/pydata/pandas) (See: Python for Data Analysis)\r\n * Python wrapper for the Twitter API [twython](https://github.com/ryanmcgrath/twython)\r\n * Tools for Data Mining & Analysis [scikit-learn](http://scikit-learn.org/stable/)\r\n * Network Modeling & Viz [networkx](http://networkx.github.io/)\r\n * Natural Language Toolkit [NLTK](http://nltk.org/)\r\n\r\n### Capstone Project\r\n* [Toy Data Ideas](www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Capstone Analysis of Your Own Design\r\n * (ideas?)\r\n\r\n\r\n***\r\n### Further Study Resources:\r\n* [Coursera](http://coursera.org)\r\n* [Khan Academy](https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/term-life-insurance-and-death-probability)\r\n* [Wolfram Alpha](http://www.wolframalpha.com/input/?i=torus)\r\n* [Wikipedia](http://en.wikipedia.org/wiki/List_of_cognitive_biases)\r\n* [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Kindle .mobis\r\n* Fave PopSci Read: [The Signal and The Noise](http://www.amazon.com/Signal-Noise-Predictions-Fail-but-ebook/dp/B007V65R54/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=8-1&qid=1376699450) Nate Silver\r\n\r\n## Contribute\r\n\r\nI'm not the expert -- please Share and Contribute. **It's Open Source**!\r\n\r\nTake a look at [my transcript](https://github.com/datasciencemasters/go/wiki/%5BTranscript%5D-Clare-Corthell); Please **showcase your transcript on the [wiki](https://github.com/datasciencemasters/go/wiki) too!**\r\n\r\n[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)","google":"","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The Open Source Data Science Masters","tagline":"Curriculum","body":"The Curriculum for learning Data Science, Open Source and at your fingertips.\r\n\r\n*NB: Wikis are easier to edit, so the **community version** is found [HERE](https://github.com/datasciencemasters/go/wiki).*\r\n\r\n## The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*See [My Forked Transcript](http://bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n## The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://blog.bright.com/2012/11/13/how-to-hire-a-data-scientist/) 13 Nov 2012\r\n\r\n## An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n**Ready?**\r\n\r\n***\r\n\r\n## The How: An Open Source Data Science Curriculum\r\n\r\nStart here.\r\n* **Intro to Data Science** [UW / Coursera](https://www.coursera.org/course/datasci)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n### Math\r\n* Linear Algebra / Levandosky [Stanford / Book](http://www.amazon.com/Linear-Algebra-Steven-Levandosky/dp/0536667470/ref=sr_1_1?ie=UTF8&qid=1376546498&sr=8-1&keywords=linear+algebra+levandosky#)\r\n* Statistics [Stats in a Nutshell / Book](http://shop.oreilly.com/product/9780596510497.do)\r\n* Problem-Solving Heuristics \"How To Solve It\" [Polya / Book](http://en.wikipedia.org/wiki/How_to_Solve_It)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](https://www.coursera.org/course/algo)\r\n * Algorithm Design [Kleinberg & Tardos / Book](http://www.amazon.com/Algorithm-Design-Jon-Kleinberg/dp/0321295358/ref=sr_1_1?ie=UTF8&qid=1376702127&sr=8-1&keywords=kleinberg+algorithms)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Coursera](https://www.coursera.org/course/db)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Book](http://i.stanford.edu/~ullman/mmds.html)\r\n * Mining The Social Web [O'Reilly / Book](http://shop.oreilly.com/product/0636920010203.do)\r\n * Introduction to Information Retrieval [Stanford / Book](http://nlp.stanford.edu/IR-book/information-retrieval-book.html)\r\n\r\n* **Machine Learning**\r\n * Machine Learning / Ng [Stanford / Coursera](https://www.coursera.org/course/ml)\r\n * Programming Collective Intelligence [O'Reilly / Book]()\r\n * Statistics [The Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) #current\r\n\r\n* **Probabilistic Graphical Models**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)\r\n * PGMs / Koller [Stanford / Coursera](https://www.coursera.org/course/pgm)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python [O'Reilly / Book](http://shop.oreilly.com/product/9780596516499.do)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [O'Reilly / Book](http://www.kqzyfj.com/click-7040302-11260198?url=http%3A%2F%2Fshop.oreilly.com%2Fproduct%2F0636920023784.do&cjsku=0636920023784)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://blogs.ischool.berkeley.edu/i290-abdt-s12/)\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](https://www.coursera.org/course/networksonline)\r\n * Information Visualization [\"Envisioning Information\" Tufte / Book](http://www.amazon.com/Envisioning-Information-Edward-R-Tufte/dp/0961392118/ref=sr_1_8?ie=UTF8&qid=1376709039&sr=8-8&keywords=information+design)\r\n\r\n* **Python** (Learning)\r\n * New To Python: [Learn Python the Hard Way](http://learnpythonthehardway.org/), [Google's Python Class](code.google.com/edu/languages/google-python-class/)\r\n\r\n* **Python** (Libraries)\r\n * Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://www.lowindata.com/2013/installing-scientific-python-on-mac-os-x/)\r\n * Bayesian Inference | [pymc](https://github.com/pymc-devs/pymc)\r\n * Labeled data structures objects, statistical functions, etc [pandas](https://github.com/pydata/pandas) (See: Python for Data Analysis)\r\n * Python wrapper for the Twitter API [twython](https://github.com/ryanmcgrath/twython)\r\n * Tools for Data Mining & Analysis [scikit-learn](http://scikit-learn.org/stable/)\r\n * Network Modeling & Viz [networkx](http://networkx.github.io/)\r\n * Natural Language Toolkit [NLTK](http://nltk.org/)\r\n\r\n### Capstone Project\r\n* [Toy Data Ideas](www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Capstone Analysis of Your Own Design; [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)'s Idea Compendium\r\n\r\n\r\n***\r\n### Further Study Resources:\r\n* [Coursera](http://coursera.org)\r\n* [Khan Academy](https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/term-life-insurance-and-death-probability)\r\n* [Wolfram Alpha](http://www.wolframalpha.com/input/?i=torus)\r\n* [Wikipedia](http://en.wikipedia.org/wiki/List_of_cognitive_biases)\r\n* [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Kindle .mobis\r\n* Fave PopSci Read: [The Signal and The Noise](http://www.amazon.com/Signal-Noise-Predictions-Fail-but-ebook/dp/B007V65R54/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=8-1&qid=1376699450) Nate Silver\r\n* Zipfian Academy's [List of Resources](http://blog.zipfianacademy.com/post/46864003608/a-practical-intro-to-data-science)\r\n* [A Software Engineer's Guide to Getting Started w Data Science](http://www.rcasts.com/2012/12/software-engineers-guide-to-getting.html)\r\n\r\n## Contribute\r\n\r\nI'm not the expert -- please Share and Contribute. **It's Open Source**!\r\n\r\nHere's [my transcript](https://github.com/datasciencemasters/go/wiki/%5BTranscript%5D-Clare-Corthell); Please **showcase your own** on the [wiki](https://github.com/datasciencemasters/go/wiki)!\r\n\r\n[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file From 87f94e25925568089fc0f972029c3fdb8bc0e1a2 Mon Sep 17 00:00:00 2001 From: Clare Date: Tue, 3 Sep 2013 14:23:18 -0700 Subject: [PATCH 14/35] Create gh-pages branch via GitHub --- index.html | 2 +- params.json | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/index.html b/index.html index 0f4692ae..70ffa987 100644 --- a/index.html +++ b/index.html @@ -191,7 +191,7 @@

    Python (Learning)

    diff --git a/params.json b/params.json index 91d09e7d..c8079dbf 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"The Open Source Data Science Masters","tagline":"Curriculum","body":"The Curriculum for learning Data Science, Open Source and at your fingertips.\r\n\r\n*NB: Wikis are easier to edit, so the **community version** is found [HERE](https://github.com/datasciencemasters/go/wiki).*\r\n\r\n## The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*See [My Forked Transcript](http://bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n## The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://blog.bright.com/2012/11/13/how-to-hire-a-data-scientist/) 13 Nov 2012\r\n\r\n## An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n**Ready?**\r\n\r\n***\r\n\r\n## The How: An Open Source Data Science Curriculum\r\n\r\nStart here.\r\n* **Intro to Data Science** [UW / Coursera](https://www.coursera.org/course/datasci)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n### Math\r\n* Linear Algebra / Levandosky [Stanford / Book](http://www.amazon.com/Linear-Algebra-Steven-Levandosky/dp/0536667470/ref=sr_1_1?ie=UTF8&qid=1376546498&sr=8-1&keywords=linear+algebra+levandosky#)\r\n* Statistics [Stats in a Nutshell / Book](http://shop.oreilly.com/product/9780596510497.do)\r\n* Problem-Solving Heuristics \"How To Solve It\" [Polya / Book](http://en.wikipedia.org/wiki/How_to_Solve_It)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](https://www.coursera.org/course/algo)\r\n * Algorithm Design [Kleinberg & Tardos / Book](http://www.amazon.com/Algorithm-Design-Jon-Kleinberg/dp/0321295358/ref=sr_1_1?ie=UTF8&qid=1376702127&sr=8-1&keywords=kleinberg+algorithms)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Coursera](https://www.coursera.org/course/db)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Book](http://i.stanford.edu/~ullman/mmds.html)\r\n * Mining The Social Web [O'Reilly / Book](http://shop.oreilly.com/product/0636920010203.do)\r\n * Introduction to Information Retrieval [Stanford / Book](http://nlp.stanford.edu/IR-book/information-retrieval-book.html)\r\n\r\n* **Machine Learning**\r\n * Machine Learning / Ng [Stanford / Coursera](https://www.coursera.org/course/ml)\r\n * Programming Collective Intelligence [O'Reilly / Book]()\r\n * Statistics [The Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) #current\r\n\r\n* **Probabilistic Graphical Models**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)\r\n * PGMs / Koller [Stanford / Coursera](https://www.coursera.org/course/pgm)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python [O'Reilly / Book](http://shop.oreilly.com/product/9780596516499.do)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [O'Reilly / Book](http://www.kqzyfj.com/click-7040302-11260198?url=http%3A%2F%2Fshop.oreilly.com%2Fproduct%2F0636920023784.do&cjsku=0636920023784)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://blogs.ischool.berkeley.edu/i290-abdt-s12/)\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](https://www.coursera.org/course/networksonline)\r\n * Information Visualization [\"Envisioning Information\" Tufte / Book](http://www.amazon.com/Envisioning-Information-Edward-R-Tufte/dp/0961392118/ref=sr_1_8?ie=UTF8&qid=1376709039&sr=8-8&keywords=information+design)\r\n\r\n* **Python** (Learning)\r\n * New To Python: [Learn Python the Hard Way](http://learnpythonthehardway.org/), [Google's Python Class](code.google.com/edu/languages/google-python-class/)\r\n\r\n* **Python** (Libraries)\r\n * Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://www.lowindata.com/2013/installing-scientific-python-on-mac-os-x/)\r\n * Bayesian Inference | [pymc](https://github.com/pymc-devs/pymc)\r\n * Labeled data structures objects, statistical functions, etc [pandas](https://github.com/pydata/pandas) (See: Python for Data Analysis)\r\n * Python wrapper for the Twitter API [twython](https://github.com/ryanmcgrath/twython)\r\n * Tools for Data Mining & Analysis [scikit-learn](http://scikit-learn.org/stable/)\r\n * Network Modeling & Viz [networkx](http://networkx.github.io/)\r\n * Natural Language Toolkit [NLTK](http://nltk.org/)\r\n\r\n### Capstone Project\r\n* [Toy Data Ideas](www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Capstone Analysis of Your Own Design; [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)'s Idea Compendium\r\n\r\n\r\n***\r\n### Further Study Resources:\r\n* [Coursera](http://coursera.org)\r\n* [Khan Academy](https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/term-life-insurance-and-death-probability)\r\n* [Wolfram Alpha](http://www.wolframalpha.com/input/?i=torus)\r\n* [Wikipedia](http://en.wikipedia.org/wiki/List_of_cognitive_biases)\r\n* [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Kindle .mobis\r\n* Fave PopSci Read: [The Signal and The Noise](http://www.amazon.com/Signal-Noise-Predictions-Fail-but-ebook/dp/B007V65R54/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=8-1&qid=1376699450) Nate Silver\r\n* Zipfian Academy's [List of Resources](http://blog.zipfianacademy.com/post/46864003608/a-practical-intro-to-data-science)\r\n* [A Software Engineer's Guide to Getting Started w Data Science](http://www.rcasts.com/2012/12/software-engineers-guide-to-getting.html)\r\n\r\n## Contribute\r\n\r\nI'm not the expert -- please Share and Contribute. **It's Open Source**!\r\n\r\nHere's [my transcript](https://github.com/datasciencemasters/go/wiki/%5BTranscript%5D-Clare-Corthell); Please **showcase your own** on the [wiki](https://github.com/datasciencemasters/go/wiki)!\r\n\r\n[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The Open Source Data Science Masters","tagline":"Curriculum","body":"The Curriculum for learning Data Science, Open Source and at your fingertips.\r\n\r\n*NB: Wikis are easier to edit, so the **community version** is found [HERE](https://github.com/datasciencemasters/go/wiki).*\r\n\r\n## The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*See [My Forked Transcript](http://bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n## The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://blog.bright.com/2012/11/13/how-to-hire-a-data-scientist/) 13 Nov 2012\r\n\r\n## An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n**Ready?**\r\n\r\n***\r\n\r\n## The How: An Open Source Data Science Curriculum\r\n\r\nStart here.\r\n* **Intro to Data Science** [UW / Coursera](https://www.coursera.org/course/datasci)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n### Math\r\n* Linear Algebra / Levandosky [Stanford / Book](http://www.amazon.com/Linear-Algebra-Steven-Levandosky/dp/0536667470/ref=sr_1_1?ie=UTF8&qid=1376546498&sr=8-1&keywords=linear+algebra+levandosky#)\r\n* Statistics [Stats in a Nutshell / Book](http://shop.oreilly.com/product/9780596510497.do)\r\n* Problem-Solving Heuristics \"How To Solve It\" [Polya / Book](http://en.wikipedia.org/wiki/How_to_Solve_It)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](https://www.coursera.org/course/algo)\r\n * Algorithm Design [Kleinberg & Tardos / Book](http://www.amazon.com/Algorithm-Design-Jon-Kleinberg/dp/0321295358/ref=sr_1_1?ie=UTF8&qid=1376702127&sr=8-1&keywords=kleinberg+algorithms)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Coursera](https://www.coursera.org/course/db)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Book](http://i.stanford.edu/~ullman/mmds.html)\r\n * Mining The Social Web [O'Reilly / Book](http://shop.oreilly.com/product/0636920010203.do)\r\n * Introduction to Information Retrieval [Stanford / Book](http://nlp.stanford.edu/IR-book/information-retrieval-book.html)\r\n\r\n* **Machine Learning**\r\n * Machine Learning / Ng [Stanford / Coursera](https://www.coursera.org/course/ml)\r\n * Programming Collective Intelligence [O'Reilly / Book]()\r\n * Statistics [The Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) #current\r\n\r\n* **Probabilistic Graphical Models**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)\r\n * PGMs / Koller [Stanford / Coursera](https://www.coursera.org/course/pgm)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python [O'Reilly / Book](http://shop.oreilly.com/product/9780596516499.do)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [O'Reilly / Book](http://www.kqzyfj.com/click-7040302-11260198?url=http%3A%2F%2Fshop.oreilly.com%2Fproduct%2F0636920023784.do&cjsku=0636920023784)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://blogs.ischool.berkeley.edu/i290-abdt-s12/)\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](https://www.coursera.org/course/networksonline)\r\n * Information Visualization [\"Envisioning Information\" Tufte / Book](http://www.amazon.com/Envisioning-Information-Edward-R-Tufte/dp/0961392118/ref=sr_1_8?ie=UTF8&qid=1376709039&sr=8-8&keywords=information+design)\r\n\r\n* **Python** (Learning)\r\n * New To Python: [Learn Python the Hard Way](http://learnpythonthehardway.org/), [Google's Python Class](http://code.google.com/edu/languages/google-python-class/)\r\n\r\n* **Python** (Libraries)\r\n * Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://www.lowindata.com/2013/installing-scientific-python-on-mac-os-x/)\r\n * Bayesian Inference | [pymc](https://github.com/pymc-devs/pymc)\r\n * Labeled data structures objects, statistical functions, etc [pandas](https://github.com/pydata/pandas) (See: Python for Data Analysis)\r\n * Python wrapper for the Twitter API [twython](https://github.com/ryanmcgrath/twython)\r\n * Tools for Data Mining & Analysis [scikit-learn](http://scikit-learn.org/stable/)\r\n * Network Modeling & Viz [networkx](http://networkx.github.io/)\r\n * Natural Language Toolkit [NLTK](http://nltk.org/)\r\n\r\n### Capstone Project\r\n* [Toy Data Ideas](www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Capstone Analysis of Your Own Design; [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)'s Idea Compendium\r\n\r\n\r\n***\r\n### Further Study Resources:\r\n* [Coursera](http://coursera.org)\r\n* [Khan Academy](https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/term-life-insurance-and-death-probability)\r\n* [Wolfram Alpha](http://www.wolframalpha.com/input/?i=torus)\r\n* [Wikipedia](http://en.wikipedia.org/wiki/List_of_cognitive_biases)\r\n* [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Kindle .mobis\r\n* Fave PopSci Read: [The Signal and The Noise](http://www.amazon.com/Signal-Noise-Predictions-Fail-but-ebook/dp/B007V65R54/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=8-1&qid=1376699450) Nate Silver\r\n* Zipfian Academy's [List of Resources](http://blog.zipfianacademy.com/post/46864003608/a-practical-intro-to-data-science)\r\n* [A Software Engineer's Guide to Getting Started w Data Science](http://www.rcasts.com/2012/12/software-engineers-guide-to-getting.html)\r\n\r\n## Contribute\r\n\r\nI'm not the expert -- please Share and Contribute. **It's Open Source**!\r\n\r\nHere's [my transcript](https://github.com/datasciencemasters/go/wiki/%5BTranscript%5D-Clare-Corthell); Please **showcase your own** on the [wiki](https://github.com/datasciencemasters/go/wiki)!\r\n\r\n[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file From 8b5fd7023687e3a48e7e07ba24cbc379bd3bc51a Mon Sep 17 00:00:00 2001 From: Clare Date: Tue, 3 Sep 2013 14:30:06 -0700 Subject: [PATCH 15/35] Create gh-pages branch via GitHub --- index.html | 7 ++++--- params.json | 2 +- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/index.html b/index.html index 70ffa987..b4e8755e 100644 --- a/index.html +++ b/index.html @@ -150,9 +150,10 @@

  • @@ -218,7 +219,7 @@

    Capstone Project

    Further Study Resources:

    diff --git a/params.json b/params.json index c8079dbf..77cc2adb 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"The Open Source Data Science Masters","tagline":"Curriculum","body":"The Curriculum for learning Data Science, Open Source and at your fingertips.\r\n\r\n*NB: Wikis are easier to edit, so the **community version** is found [HERE](https://github.com/datasciencemasters/go/wiki).*\r\n\r\n## The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*See [My Forked Transcript](http://bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n## The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://blog.bright.com/2012/11/13/how-to-hire-a-data-scientist/) 13 Nov 2012\r\n\r\n## An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n**Ready?**\r\n\r\n***\r\n\r\n## The How: An Open Source Data Science Curriculum\r\n\r\nStart here.\r\n* **Intro to Data Science** [UW / Coursera](https://www.coursera.org/course/datasci)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n### Math\r\n* Linear Algebra / Levandosky [Stanford / Book](http://www.amazon.com/Linear-Algebra-Steven-Levandosky/dp/0536667470/ref=sr_1_1?ie=UTF8&qid=1376546498&sr=8-1&keywords=linear+algebra+levandosky#)\r\n* Statistics [Stats in a Nutshell / Book](http://shop.oreilly.com/product/9780596510497.do)\r\n* Problem-Solving Heuristics \"How To Solve It\" [Polya / Book](http://en.wikipedia.org/wiki/How_to_Solve_It)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](https://www.coursera.org/course/algo)\r\n * Algorithm Design [Kleinberg & Tardos / Book](http://www.amazon.com/Algorithm-Design-Jon-Kleinberg/dp/0321295358/ref=sr_1_1?ie=UTF8&qid=1376702127&sr=8-1&keywords=kleinberg+algorithms)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Coursera](https://www.coursera.org/course/db)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Book](http://i.stanford.edu/~ullman/mmds.html)\r\n * Mining The Social Web [O'Reilly / Book](http://shop.oreilly.com/product/0636920010203.do)\r\n * Introduction to Information Retrieval [Stanford / Book](http://nlp.stanford.edu/IR-book/information-retrieval-book.html)\r\n\r\n* **Machine Learning**\r\n * Machine Learning / Ng [Stanford / Coursera](https://www.coursera.org/course/ml)\r\n * Programming Collective Intelligence [O'Reilly / Book]()\r\n * Statistics [The Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/) #current\r\n\r\n* **Probabilistic Graphical Models**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)\r\n * PGMs / Koller [Stanford / Coursera](https://www.coursera.org/course/pgm)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python [O'Reilly / Book](http://shop.oreilly.com/product/9780596516499.do)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [O'Reilly / Book](http://www.kqzyfj.com/click-7040302-11260198?url=http%3A%2F%2Fshop.oreilly.com%2Fproduct%2F0636920023784.do&cjsku=0636920023784)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://blogs.ischool.berkeley.edu/i290-abdt-s12/)\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](https://www.coursera.org/course/networksonline)\r\n * Information Visualization [\"Envisioning Information\" Tufte / Book](http://www.amazon.com/Envisioning-Information-Edward-R-Tufte/dp/0961392118/ref=sr_1_8?ie=UTF8&qid=1376709039&sr=8-8&keywords=information+design)\r\n\r\n* **Python** (Learning)\r\n * New To Python: [Learn Python the Hard Way](http://learnpythonthehardway.org/), [Google's Python Class](http://code.google.com/edu/languages/google-python-class/)\r\n\r\n* **Python** (Libraries)\r\n * Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://www.lowindata.com/2013/installing-scientific-python-on-mac-os-x/)\r\n * Bayesian Inference | [pymc](https://github.com/pymc-devs/pymc)\r\n * Labeled data structures objects, statistical functions, etc [pandas](https://github.com/pydata/pandas) (See: Python for Data Analysis)\r\n * Python wrapper for the Twitter API [twython](https://github.com/ryanmcgrath/twython)\r\n * Tools for Data Mining & Analysis [scikit-learn](http://scikit-learn.org/stable/)\r\n * Network Modeling & Viz [networkx](http://networkx.github.io/)\r\n * Natural Language Toolkit [NLTK](http://nltk.org/)\r\n\r\n### Capstone Project\r\n* [Toy Data Ideas](www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Capstone Analysis of Your Own Design; [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)'s Idea Compendium\r\n\r\n\r\n***\r\n### Further Study Resources:\r\n* [Coursera](http://coursera.org)\r\n* [Khan Academy](https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/term-life-insurance-and-death-probability)\r\n* [Wolfram Alpha](http://www.wolframalpha.com/input/?i=torus)\r\n* [Wikipedia](http://en.wikipedia.org/wiki/List_of_cognitive_biases)\r\n* [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Kindle .mobis\r\n* Fave PopSci Read: [The Signal and The Noise](http://www.amazon.com/Signal-Noise-Predictions-Fail-but-ebook/dp/B007V65R54/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=8-1&qid=1376699450) Nate Silver\r\n* Zipfian Academy's [List of Resources](http://blog.zipfianacademy.com/post/46864003608/a-practical-intro-to-data-science)\r\n* [A Software Engineer's Guide to Getting Started w Data Science](http://www.rcasts.com/2012/12/software-engineers-guide-to-getting.html)\r\n\r\n## Contribute\r\n\r\nI'm not the expert -- please Share and Contribute. **It's Open Source**!\r\n\r\nHere's [my transcript](https://github.com/datasciencemasters/go/wiki/%5BTranscript%5D-Clare-Corthell); Please **showcase your own** on the [wiki](https://github.com/datasciencemasters/go/wiki)!\r\n\r\n[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The Open Source Data Science Masters","tagline":"Curriculum","body":"The Curriculum for learning Data Science, Open Source and at your fingertips.\r\n\r\n*NB: Wikis are easier to edit, so the **community version** is found [HERE](https://github.com/datasciencemasters/go/wiki).*\r\n\r\n## The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*See [My Forked Transcript](http://bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n## The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://blog.bright.com/2012/11/13/how-to-hire-a-data-scientist/) 13 Nov 2012\r\n\r\n## An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n**Ready?**\r\n\r\n***\r\n\r\n## The How: An Open Source Data Science Curriculum\r\n\r\nStart here.\r\n* **Intro to Data Science** [UW / Coursera](https://www.coursera.org/course/datasci)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n### Math\r\n* Linear Algebra / Levandosky [Stanford / Book](http://www.amazon.com/Linear-Algebra-Steven-Levandosky/dp/0536667470/ref=sr_1_1?ie=UTF8&qid=1376546498&sr=8-1&keywords=linear+algebra+levandosky#)\r\n* Statistics [Stats in a Nutshell / Book](http://shop.oreilly.com/product/9780596510497.do)\r\n* Problem-Solving Heuristics \"How To Solve It\" [Polya / Book](http://en.wikipedia.org/wiki/How_to_Solve_It)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](https://www.coursera.org/course/algo)\r\n * Algorithm Design [Kleinberg & Tardos / Book](http://www.amazon.com/Algorithm-Design-Jon-Kleinberg/dp/0321295358/ref=sr_1_1?ie=UTF8&qid=1376702127&sr=8-1&keywords=kleinberg+algorithms)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Coursera](https://www.coursera.org/course/db)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Book](http://i.stanford.edu/~ullman/mmds.html)\r\n * Mining The Social Web [O'Reilly / Book](http://shop.oreilly.com/product/0636920010203.do)\r\n * Introduction to Information Retrieval [Stanford / Book](http://nlp.stanford.edu/IR-book/information-retrieval-book.html)\r\n\r\n* **Machine Learning**\r\n * Machine Learning / Ng [Stanford / Coursera](https://www.coursera.org/course/ml)\r\n * Programming Collective Intelligence [O'Reilly / Book](http://shop.oreilly.com/product/9780596529321.do)\r\n * Statistics [The Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/)\r\n\r\n* **Probabilistic Graphical Models**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)\r\n * PGMs / Koller [Stanford / Coursera](https://www.coursera.org/course/pgm)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python [O'Reilly / Book](http://shop.oreilly.com/product/9780596516499.do)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [O'Reilly / Book](http://www.kqzyfj.com/click-7040302-11260198?url=http%3A%2F%2Fshop.oreilly.com%2Fproduct%2F0636920023784.do&cjsku=0636920023784)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://blogs.ischool.berkeley.edu/i290-abdt-s12/)\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](https://www.coursera.org/course/networksonline)\r\n * Information Visualization [\"Envisioning Information\" Tufte / Book](http://www.amazon.com/Envisioning-Information-Edward-R-Tufte/dp/0961392118/ref=sr_1_8?ie=UTF8&qid=1376709039&sr=8-8&keywords=information+design)\r\n\r\n* **Python** (Learning)\r\n * New To Python: [Learn Python the Hard Way](http://learnpythonthehardway.org/), [Google's Python Class](http://code.google.com/edu/languages/google-python-class/)\r\n\r\n* **Python** (Libraries)\r\n * Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://www.lowindata.com/2013/installing-scientific-python-on-mac-os-x/)\r\n * Bayesian Inference | [pymc](https://github.com/pymc-devs/pymc)\r\n * Labeled data structures objects, statistical functions, etc [pandas](https://github.com/pydata/pandas) (See: Python for Data Analysis)\r\n * Python wrapper for the Twitter API [twython](https://github.com/ryanmcgrath/twython)\r\n * Tools for Data Mining & Analysis [scikit-learn](http://scikit-learn.org/stable/)\r\n * Network Modeling & Viz [networkx](http://networkx.github.io/)\r\n * Natural Language Toolkit [NLTK](http://nltk.org/)\r\n\r\n### Capstone Project\r\n* [Toy Data Ideas](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Capstone Analysis of Your Own Design; [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)'s Idea Compendium\r\n\r\n\r\n***\r\n### Further Study Resources:\r\n* [Coursera](http://coursera.org)\r\n* [Khan Academy](https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/term-life-insurance-and-death-probability)\r\n* [Wolfram Alpha](http://www.wolframalpha.com/input/?i=torus)\r\n* [Wikipedia](http://en.wikipedia.org/wiki/List_of_cognitive_biases)\r\n* [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Kindle .mobis\r\n* Fave PopSci Read: [The Signal and The Noise](http://www.amazon.com/Signal-Noise-Predictions-Fail-but-ebook/dp/B007V65R54/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=8-1&qid=1376699450) Nate Silver\r\n* Zipfian Academy's [List of Resources](http://blog.zipfianacademy.com/post/46864003608/a-practical-intro-to-data-science)\r\n* [A Software Engineer's Guide to Getting Started w Data Science](http://www.rcasts.com/2012/12/software-engineers-guide-to-getting.html)\r\n\r\n## Contribute\r\n\r\nI'm not the expert -- please Share and Contribute. **It's Open Source**!\r\n\r\nHere's [my transcript](https://github.com/datasciencemasters/go/wiki/%5BTranscript%5D-Clare-Corthell); Please **showcase your own** on the [wiki](https://github.com/datasciencemasters/go/wiki)!\r\n\r\n[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file From 4859aef33d8f11251d02a36322049e5ab4c23bf7 Mon Sep 17 00:00:00 2001 From: Clare Date: Fri, 6 Sep 2013 11:25:49 -0700 Subject: [PATCH 16/35] Create gh-pages branch via GitHub --- index.html | 51 ++++++++++++++++++++++++++++++++++++++------------- params.json | 2 +- 2 files changed, 39 insertions(+), 14 deletions(-) diff --git a/index.html b/index.html index b4e8755e..9d412ccf 100644 --- a/index.html +++ b/index.html @@ -41,19 +41,24 @@

    The Open Source Data Science Masters

    The Curriculum for learning Data Science, Open Source and at your fingertips.

    -

    NB: Wikis are easier to edit, so the **community version* is found HERE.*

    +
    +NB: "More" Living Doc HERE +

    -The Internet is Your Oyster

    +The Open-Source Data Science Masters - Curriculum

    • + +

    +The Internet is Your Oyster

    I didn't want to wait. I wanted to work on things I care about now. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?

    -

    See My Forked Transcript

    +

    see my transcript

    With Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?

    -

    -The Motivation

    +

    +The Motivation

    We need more Data Scientists.

    @@ -69,8 +74,8 @@

    -- David Hardtke How To Hire A Data Scientist 13 Nov 2012

    -

    -An Academic Shortfall

    +

    +An Academic Shortfall

    Classic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.

    @@ -84,10 +89,11 @@

    -- James Kobielus, Closing the Talent Gap 17 Jan 2013

    -

    Ready?

    +

    +Ready?

    -The How: An Open Source Data Science Curriculum

    +The Open Source Data Science Curriculum

    Start here.

    @@ -106,10 +112,17 @@

    Computing

    @@ -130,6 +143,8 @@

  • @@ -202,6 +217,8 @@

    +
  • Data Scientist Interviews Metamarkets +
    • +

    +A Note About Direction

    + +

    This is an introduction geared toward those with at least a minimal understanding of programming, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing). +Out of personal preference and need for focus, I geared the original curriculum toward Python tools and resources, so I've explicitly marked when resources use other tools to teach conceptual material (like R)

    + +

    Contribute

    -

    I'm not the expert -- please Share and Contribute. It's Open Source!

    +

    Please Share and Contribute Your Ideas -- it's Open Source!

    -

    Here's my transcript; Please showcase your own on the wiki!

    +

    Here's my transcript; Please showcase your own on the wiki!

    Follow me on Twitter @clarecorthell

    diff --git a/params.json b/params.json index 77cc2adb..79f4aca0 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"The Open Source Data Science Masters","tagline":"Curriculum","body":"The Curriculum for learning Data Science, Open Source and at your fingertips.\r\n\r\n*NB: Wikis are easier to edit, so the **community version** is found [HERE](https://github.com/datasciencemasters/go/wiki).*\r\n\r\n## The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*See [My Forked Transcript](http://bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n## The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://blog.bright.com/2012/11/13/how-to-hire-a-data-scientist/) 13 Nov 2012\r\n\r\n## An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n**Ready?**\r\n\r\n***\r\n\r\n## The How: An Open Source Data Science Curriculum\r\n\r\nStart here.\r\n* **Intro to Data Science** [UW / Coursera](https://www.coursera.org/course/datasci)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n### Math\r\n* Linear Algebra / Levandosky [Stanford / Book](http://www.amazon.com/Linear-Algebra-Steven-Levandosky/dp/0536667470/ref=sr_1_1?ie=UTF8&qid=1376546498&sr=8-1&keywords=linear+algebra+levandosky#)\r\n* Statistics [Stats in a Nutshell / Book](http://shop.oreilly.com/product/9780596510497.do)\r\n* Problem-Solving Heuristics \"How To Solve It\" [Polya / Book](http://en.wikipedia.org/wiki/How_to_Solve_It)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](https://www.coursera.org/course/algo)\r\n * Algorithm Design [Kleinberg & Tardos / Book](http://www.amazon.com/Algorithm-Design-Jon-Kleinberg/dp/0321295358/ref=sr_1_1?ie=UTF8&qid=1376702127&sr=8-1&keywords=kleinberg+algorithms)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Coursera](https://www.coursera.org/course/db)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Book](http://i.stanford.edu/~ullman/mmds.html)\r\n * Mining The Social Web [O'Reilly / Book](http://shop.oreilly.com/product/0636920010203.do)\r\n * Introduction to Information Retrieval [Stanford / Book](http://nlp.stanford.edu/IR-book/information-retrieval-book.html)\r\n\r\n* **Machine Learning**\r\n * Machine Learning / Ng [Stanford / Coursera](https://www.coursera.org/course/ml)\r\n * Programming Collective Intelligence [O'Reilly / Book](http://shop.oreilly.com/product/9780596529321.do)\r\n * Statistics [The Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/)\r\n\r\n* **Probabilistic Graphical Models**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)\r\n * PGMs / Koller [Stanford / Coursera](https://www.coursera.org/course/pgm)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python [O'Reilly / Book](http://shop.oreilly.com/product/9780596516499.do)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [O'Reilly / Book](http://www.kqzyfj.com/click-7040302-11260198?url=http%3A%2F%2Fshop.oreilly.com%2Fproduct%2F0636920023784.do&cjsku=0636920023784)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://blogs.ischool.berkeley.edu/i290-abdt-s12/)\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](https://www.coursera.org/course/networksonline)\r\n * Information Visualization [\"Envisioning Information\" Tufte / Book](http://www.amazon.com/Envisioning-Information-Edward-R-Tufte/dp/0961392118/ref=sr_1_8?ie=UTF8&qid=1376709039&sr=8-8&keywords=information+design)\r\n\r\n* **Python** (Learning)\r\n * New To Python: [Learn Python the Hard Way](http://learnpythonthehardway.org/), [Google's Python Class](http://code.google.com/edu/languages/google-python-class/)\r\n\r\n* **Python** (Libraries)\r\n * Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://www.lowindata.com/2013/installing-scientific-python-on-mac-os-x/)\r\n * Bayesian Inference | [pymc](https://github.com/pymc-devs/pymc)\r\n * Labeled data structures objects, statistical functions, etc [pandas](https://github.com/pydata/pandas) (See: Python for Data Analysis)\r\n * Python wrapper for the Twitter API [twython](https://github.com/ryanmcgrath/twython)\r\n * Tools for Data Mining & Analysis [scikit-learn](http://scikit-learn.org/stable/)\r\n * Network Modeling & Viz [networkx](http://networkx.github.io/)\r\n * Natural Language Toolkit [NLTK](http://nltk.org/)\r\n\r\n### Capstone Project\r\n* [Toy Data Ideas](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Capstone Analysis of Your Own Design; [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)'s Idea Compendium\r\n\r\n\r\n***\r\n### Further Study Resources:\r\n* [Coursera](http://coursera.org)\r\n* [Khan Academy](https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/term-life-insurance-and-death-probability)\r\n* [Wolfram Alpha](http://www.wolframalpha.com/input/?i=torus)\r\n* [Wikipedia](http://en.wikipedia.org/wiki/List_of_cognitive_biases)\r\n* [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Kindle .mobis\r\n* Fave PopSci Read: [The Signal and The Noise](http://www.amazon.com/Signal-Noise-Predictions-Fail-but-ebook/dp/B007V65R54/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=8-1&qid=1376699450) Nate Silver\r\n* Zipfian Academy's [List of Resources](http://blog.zipfianacademy.com/post/46864003608/a-practical-intro-to-data-science)\r\n* [A Software Engineer's Guide to Getting Started w Data Science](http://www.rcasts.com/2012/12/software-engineers-guide-to-getting.html)\r\n\r\n## Contribute\r\n\r\nI'm not the expert -- please Share and Contribute. **It's Open Source**!\r\n\r\nHere's [my transcript](https://github.com/datasciencemasters/go/wiki/%5BTranscript%5D-Clare-Corthell); Please **showcase your own** on the [wiki](https://github.com/datasciencemasters/go/wiki)!\r\n\r\n[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The Open Source Data Science Masters","tagline":"Curriculum","body":"The Curriculum for learning Data Science, Open Source and at your fingertips.\r\n\r\n###### NB: \"More\" Living Doc [HERE](http://github.com/datasciencemasters/go)\r\n\r\n## The Open-Source Data Science Masters - Curriculum\r\n\r\n### The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*see [my transcript](http://bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://blog.bright.com/2012/11/13/how-to-hire-a-data-scientist/) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n* **Intro to Data Science** [UW / Coursera](https://www.coursera.org/course/datasci)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n### Math\r\n* Linear Algebra / Levandosky [Stanford / Book](http://www.amazon.com/Linear-Algebra-Steven-Levandosky/dp/0536667470/ref=sr_1_1?ie=UTF8&qid=1376546498&sr=8-1&keywords=linear+algebra+levandosky#)\r\n* Linear Programming (Math 407) [University of Washington / Course](http://www.math.washington.edu/~burke/crs/407/lectures/)\r\n* Statistics [Stats in a Nutshell / Book](http://shop.oreilly.com/product/9780596510497.do)\r\n* Forecasting: Principles and Practice [Monash University / Book](http://otexts.com/fpp/) *uses R\r\n* Problem-Solving Heuristics \"How To Solve It\" [Polya / Book](http://en.wikipedia.org/wiki/How_to_Solve_It)\r\n* Coding the Matrix: Linear Algebra through Computer Science Applications [Brown / Coursera](https://www.coursera.org/course/matrix)\r\n* Think Bayes [Allen Downey / Book](http://www.greenteapress.com/thinkbayes/)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](https://www.coursera.org/course/algo)\r\n * Algorithm Design [Kleinberg & Tardos / Book](http://www.amazon.com/Algorithm-Design-Jon-Kleinberg/dp/0321295358/ref=sr_1_1?ie=UTF8&qid=1376702127&sr=8-1&keywords=kleinberg+algorithms)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Coursera](https://www.coursera.org/course/db)\r\n * SQL Tutorial [W3Schools / Tutorials](http://www.w3schools.com/sql/)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Book](http://i.stanford.edu/~ullman/mmds.html)\r\n * Mining The Social Web [O'Reilly / Book](http://shop.oreilly.com/product/0636920010203.do)\r\n * Introduction to Information Retrieval [Stanford / Book](http://nlp.stanford.edu/IR-book/information-retrieval-book.html)\r\n\r\n* **Machine Learning**\r\n * Machine Learning / Ng [Stanford / Coursera](https://www.coursera.org/course/ml)\r\n * Programming Collective Intelligence [O'Reilly / Book](http://shop.oreilly.com/product/9780596529321.do)\r\n * Statistics [The Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/)\r\n\r\n* **Probabilistic Graphical Models**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)\r\n * PGMs / Koller [Stanford / Coursera](https://www.coursera.org/course/pgm)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python [O'Reilly / Book](http://shop.oreilly.com/product/9780596516499.do)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [O'Reilly / Book](http://www.kqzyfj.com/click-7040302-11260198?url=http%3A%2F%2Fshop.oreilly.com%2Fproduct%2F0636920023784.do&cjsku=0636920023784)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://blogs.ischool.berkeley.edu/i290-abdt-s12/)\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](https://www.coursera.org/course/networksonline)\r\n * Information Visualization [\"Envisioning Information\" Tufte / Book](http://www.amazon.com/Envisioning-Information-Edward-R-Tufte/dp/0961392118/ref=sr_1_8?ie=UTF8&qid=1376709039&sr=8-8&keywords=information+design)\r\n\r\n* **Python** (Learning)\r\n * New To Python: [Learn Python the Hard Way](http://learnpythonthehardway.org/), [Google's Python Class](http://code.google.com/edu/languages/google-python-class/)\r\n\r\n* **Python** (Libraries)\r\n * Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://www.lowindata.com/2013/installing-scientific-python-on-mac-os-x/)\r\n * [Data Science in iPython Notebooks](http://nborwankar.github.io/LearnDataScience/) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * Bayesian Inference | [pymc](https://github.com/pymc-devs/pymc)\r\n * Labeled data structures objects, statistical functions, etc [pandas](https://github.com/pydata/pandas) (See: Python for Data Analysis)\r\n * Python wrapper for the Twitter API [twython](https://github.com/ryanmcgrath/twython)\r\n * Tools for Data Mining & Analysis [scikit-learn](http://scikit-learn.org/stable/)\r\n * Network Modeling & Viz [networkx](http://networkx.github.io/)\r\n * Natural Language Toolkit [NLTK](http://nltk.org/)\r\n\r\n### Capstone Project\r\n* [Toy Data Ideas](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Capstone Analysis of Your Own Design; [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)'s Idea Compendium\r\n\r\n***\r\n### Further Study Resources:\r\n* [Coursera](http://coursera.org)\r\n* [Khan Academy](https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/term-life-insurance-and-death-probability)\r\n* [Wolfram Alpha](http://www.wolframalpha.com/input/?i=torus)\r\n* [Wikipedia](http://en.wikipedia.org/wiki/List_of_cognitive_biases)\r\n* [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Kindle .mobis\r\n* Great PopSci Read: [The Signal and The Noise](http://www.amazon.com/Signal-Noise-Predictions-Fail-but-ebook/dp/B007V65R54/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=8-1&qid=1376699450) Nate Silver\r\n* Zipfian Academy's [List of Resources](http://blog.zipfianacademy.com/post/46864003608/a-practical-intro-to-data-science)\r\n* [A Software Engineer's Guide to Getting Started w Data Science](http://www.rcasts.com/2012/12/software-engineers-guide-to-getting.html)\r\n* Data Scientist Interviews [Metamarkets](http://metamarkets.com/category/data-science/)\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimal understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**, so I've explicitly marked when resources use other tools to teach conceptual material (like R)\r\n\r\n## Contribute\r\n\r\nPlease Share and Contribute Your Ideas -- **it's Open Source!**\r\n\r\nHere's [my transcript](https://github.com/datasciencemasters/go/wiki/%5BTranscript%5D-Clare-Corthell); Please **showcase your own** on the [wiki](https://github.com/datasciencemasters/go/wiki/)!\r\n\r\n[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file From fa8e61e23452e8c5b79e88659ccdf3460f26788b Mon Sep 17 00:00:00 2001 From: Clare Date: Fri, 17 Jan 2014 13:37:37 -0800 Subject: [PATCH 17/35] Create gh-pages branch via GitHub --- index.html | 98 +++++++++++++++++++++++++++++++++++++++++++++++++---- params.json | 2 +- 2 files changed, 92 insertions(+), 8 deletions(-) diff --git a/index.html b/index.html index 9d412ccf..f7d7e437 100644 --- a/index.html +++ b/index.html @@ -42,9 +42,13 @@

    The Open Source Data Science Masters

    The Curriculum for learning Data Science, Open Source and at your fingertips.

    -NB: "More" Living Doc HERE +More "Alive" Doc HERE
    +

    +Follow me on Twitter @clarecorthell +

    +

    The Open-Source Data Science Masters - Curriculum

    @@ -55,7 +59,7 @@

    see my transcript

    -

    With Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?

    +

    With Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?

    The Motivation

    @@ -123,6 +127,7 @@

  • Think Bayes Allen Downey / Book
    • +
  • Think Stats: Probability and Statistics for Programmers Allen Downey / Book

    • Computing

    @@ -138,13 +143,24 @@

  • -

    Databases

    +

    Distributed Computing Paradigms

    • +
  • +

    Databases

    + +
  • @@ -165,10 +181,14 @@

  • @@ -185,7 +205,7 @@

    Natural Language Processing

    • @@ -199,15 +219,29 @@

  • Social and Economic Networks: Models and Analysis / Stanford / Coursera
    • + + +
  • +

    Visualization

    + +

  • Python (Learning)

    • @@ -232,18 +266,60 @@

    +
  • +

    R (Learning)

    + + +
    • +
  • +

    R (Libraries)

    + + +

    • Capstone Project

    Further Study Resources:

    +Sources for Datasets:

    + +
      +
    • Competitions between Data Scientists on new datasets Kaggle +
      • +
    • Hilary Mason's List of Research-quality datasets bit.ly +
      • +

    A Note About Direction

    -

    This is an introduction geared toward those with at least a minimal understanding of programming, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing). +

    This is an introduction geared toward those with at least a minimum understanding of programming, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing). Out of personal preference and need for focus, I geared the original curriculum toward Python tools and resources, so I've explicitly marked when resources use other tools to teach conceptual material (like R)

    diff --git a/params.json b/params.json index 79f4aca0..4cc9fe16 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"The Open Source Data Science Masters","tagline":"Curriculum","body":"The Curriculum for learning Data Science, Open Source and at your fingertips.\r\n\r\n###### NB: \"More\" Living Doc [HERE](http://github.com/datasciencemasters/go)\r\n\r\n## The Open-Source Data Science Masters - Curriculum\r\n\r\n### The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*see [my transcript](http://bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, stackoverflow, and github -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://blog.bright.com/2012/11/13/how-to-hire-a-data-scientist/) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n* **Intro to Data Science** [UW / Coursera](https://www.coursera.org/course/datasci)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n### Math\r\n* Linear Algebra / Levandosky [Stanford / Book](http://www.amazon.com/Linear-Algebra-Steven-Levandosky/dp/0536667470/ref=sr_1_1?ie=UTF8&qid=1376546498&sr=8-1&keywords=linear+algebra+levandosky#)\r\n* Linear Programming (Math 407) [University of Washington / Course](http://www.math.washington.edu/~burke/crs/407/lectures/)\r\n* Statistics [Stats in a Nutshell / Book](http://shop.oreilly.com/product/9780596510497.do)\r\n* Forecasting: Principles and Practice [Monash University / Book](http://otexts.com/fpp/) *uses R\r\n* Problem-Solving Heuristics \"How To Solve It\" [Polya / Book](http://en.wikipedia.org/wiki/How_to_Solve_It)\r\n* Coding the Matrix: Linear Algebra through Computer Science Applications [Brown / Coursera](https://www.coursera.org/course/matrix)\r\n* Think Bayes [Allen Downey / Book](http://www.greenteapress.com/thinkbayes/)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](https://www.coursera.org/course/algo)\r\n * Algorithm Design [Kleinberg & Tardos / Book](http://www.amazon.com/Algorithm-Design-Jon-Kleinberg/dp/0321295358/ref=sr_1_1?ie=UTF8&qid=1376702127&sr=8-1&keywords=kleinberg+algorithms)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Coursera](https://www.coursera.org/course/db)\r\n * SQL Tutorial [W3Schools / Tutorials](http://www.w3schools.com/sql/)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Book](http://i.stanford.edu/~ullman/mmds.html)\r\n * Mining The Social Web [O'Reilly / Book](http://shop.oreilly.com/product/0636920010203.do)\r\n * Introduction to Information Retrieval [Stanford / Book](http://nlp.stanford.edu/IR-book/information-retrieval-book.html)\r\n\r\n* **Machine Learning**\r\n * Machine Learning / Ng [Stanford / Coursera](https://www.coursera.org/course/ml)\r\n * Programming Collective Intelligence [O'Reilly / Book](http://shop.oreilly.com/product/9780596529321.do)\r\n * Statistics [The Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/)\r\n\r\n* **Probabilistic Graphical Models**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)\r\n * PGMs / Koller [Stanford / Coursera](https://www.coursera.org/course/pgm)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python [O'Reilly / Book](http://shop.oreilly.com/product/9780596516499.do)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [O'Reilly / Book](http://www.kqzyfj.com/click-7040302-11260198?url=http%3A%2F%2Fshop.oreilly.com%2Fproduct%2F0636920023784.do&cjsku=0636920023784)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://blogs.ischool.berkeley.edu/i290-abdt-s12/)\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](https://www.coursera.org/course/networksonline)\r\n * Information Visualization [\"Envisioning Information\" Tufte / Book](http://www.amazon.com/Envisioning-Information-Edward-R-Tufte/dp/0961392118/ref=sr_1_8?ie=UTF8&qid=1376709039&sr=8-8&keywords=information+design)\r\n\r\n* **Python** (Learning)\r\n * New To Python: [Learn Python the Hard Way](http://learnpythonthehardway.org/), [Google's Python Class](http://code.google.com/edu/languages/google-python-class/)\r\n\r\n* **Python** (Libraries)\r\n * Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://www.lowindata.com/2013/installing-scientific-python-on-mac-os-x/)\r\n * [Data Science in iPython Notebooks](http://nborwankar.github.io/LearnDataScience/) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * Bayesian Inference | [pymc](https://github.com/pymc-devs/pymc)\r\n * Labeled data structures objects, statistical functions, etc [pandas](https://github.com/pydata/pandas) (See: Python for Data Analysis)\r\n * Python wrapper for the Twitter API [twython](https://github.com/ryanmcgrath/twython)\r\n * Tools for Data Mining & Analysis [scikit-learn](http://scikit-learn.org/stable/)\r\n * Network Modeling & Viz [networkx](http://networkx.github.io/)\r\n * Natural Language Toolkit [NLTK](http://nltk.org/)\r\n\r\n### Capstone Project\r\n* [Toy Data Ideas](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Capstone Analysis of Your Own Design; [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)'s Idea Compendium\r\n\r\n***\r\n### Further Study Resources:\r\n* [Coursera](http://coursera.org)\r\n* [Khan Academy](https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/term-life-insurance-and-death-probability)\r\n* [Wolfram Alpha](http://www.wolframalpha.com/input/?i=torus)\r\n* [Wikipedia](http://en.wikipedia.org/wiki/List_of_cognitive_biases)\r\n* [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Kindle .mobis\r\n* Great PopSci Read: [The Signal and The Noise](http://www.amazon.com/Signal-Noise-Predictions-Fail-but-ebook/dp/B007V65R54/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=8-1&qid=1376699450) Nate Silver\r\n* Zipfian Academy's [List of Resources](http://blog.zipfianacademy.com/post/46864003608/a-practical-intro-to-data-science)\r\n* [A Software Engineer's Guide to Getting Started w Data Science](http://www.rcasts.com/2012/12/software-engineers-guide-to-getting.html)\r\n* Data Scientist Interviews [Metamarkets](http://metamarkets.com/category/data-science/)\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimal understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**, so I've explicitly marked when resources use other tools to teach conceptual material (like R)\r\n\r\n## Contribute\r\n\r\nPlease Share and Contribute Your Ideas -- **it's Open Source!**\r\n\r\nHere's [my transcript](https://github.com/datasciencemasters/go/wiki/%5BTranscript%5D-Clare-Corthell); Please **showcase your own** on the [wiki](https://github.com/datasciencemasters/go/wiki/)!\r\n\r\n[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The Open Source Data Science Masters","tagline":"Curriculum","body":"The Curriculum for learning Data Science, Open Source and at your fingertips.\r\n\r\n###### More \"Alive\" Doc [HERE](http://github.com/datasciencemasters/go)\r\n\r\n####[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)\r\n\r\n\r\n## The Open-Source Data Science Masters - Curriculum\r\n\r\n### The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*see [my transcript](http://bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://blog.bright.com/2012/11/13/how-to-hire-a-data-scientist/) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n* **Intro to Data Science** [UW / Coursera](https://www.coursera.org/course/datasci)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n### Math\r\n* Linear Algebra / Levandosky [Stanford / Book](http://www.amazon.com/Linear-Algebra-Steven-Levandosky/dp/0536667470/ref=sr_1_1?ie=UTF8&qid=1376546498&sr=8-1&keywords=linear+algebra+levandosky#)\r\n* Linear Programming (Math 407) [University of Washington / Course](http://www.math.washington.edu/~burke/crs/407/lectures/)\r\n* Statistics [Stats in a Nutshell / Book](http://shop.oreilly.com/product/9780596510497.do)\r\n* Forecasting: Principles and Practice [Monash University / Book](http://otexts.com/fpp/) *uses R\r\n* Problem-Solving Heuristics \"How To Solve It\" [Polya / Book](http://en.wikipedia.org/wiki/How_to_Solve_It)\r\n* Coding the Matrix: Linear Algebra through Computer Science Applications [Brown / Coursera](https://www.coursera.org/course/matrix)\r\n* Think Bayes [Allen Downey / Book](http://www.greenteapress.com/thinkbayes/)\r\n* Think Stats: Probability and Statistics for Programmers [Allen Downey / Book](http://greenteapress.com/thinkstats/) \r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](https://www.coursera.org/course/algo)\r\n * Algorithm Design [Kleinberg & Tardos / Book](http://www.amazon.com/Algorithm-Design-Jon-Kleinberg/dp/0321295358/ref=sr_1_1?ie=UTF8&qid=1376702127&sr=8-1&keywords=kleinberg+algorithms)\r\n\r\n* **Distributed Computing Paradigms**\r\n * NB: The UW Intro to Data Science does a great job teaching hadoop [UW / Coursera](https://www.coursera.org/course/datasci)\r\n * [Intro to Hadoop and MapReduce](https://www.udacity.com/course/ud617)\r\n\r\n* **Databases**\r\n * SQL Tutorial [W3Schools / Tutorials](http://www.w3schools.com/sql/)\r\n * Another SQL Tutorial [SQLZOO](http://sqlzoo.net/wiki/Main_Page)\r\n * Introduction to Databases [Stanford / Online Course](http://class2go.stanford.edu/db/Winter2013/)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Book](http://i.stanford.edu/~ullman/mmds.html)\r\n * Mining The Social Web [O'Reilly / Book](http://shop.oreilly.com/product/0636920010203.do)\r\n * Introduction to Information Retrieval [Stanford / Book](http://nlp.stanford.edu/IR-book/information-retrieval-book.html)\r\n\r\n* **Machine Learning**\r\n * Machine Learning / Ng [Stanford / Coursera](https://www.coursera.org/course/ml)\r\n * A Course in Machine Learning / Hal Daumé III UMD [Online Book](http://ciml.info/)\r\n * Programming Collective Intelligence [O'Reilly / Book](http://shop.oreilly.com/product/9780596529321.do)\r\n * Statistics [The Elements of Statistical Learning](http://www-stat.stanford.edu/~tibs/ElemStatLearn/)\r\n * Machine Learning / CaltechX [Caltech / Edx](https://courses.edx.org/courses/CaltechX/CS1156x/Fall2013/)\r\n\r\n* **Probabilistic Graphical Models**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)\r\n * PGMs / Koller [Stanford / Coursera](https://www.coursera.org/course/pgm)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python [O'Reilly / Book](http://shop.oreilly.com/product/9780596516499.do), [Free online version](http://nltk.org/book/)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [O'Reilly / Book](http://www.kqzyfj.com/click-7040302-11260198?url=http%3A%2F%2Fshop.oreilly.com%2Fproduct%2F0636920023784.do&cjsku=0636920023784)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://blogs.ischool.berkeley.edu/i290-abdt-s12/)\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](https://www.coursera.org/course/networksonline)\r\n\r\n* **Visualization**\r\n * Information Visualization [\"Envisioning Information\" Tufte / Book](http://www.amazon.com/Envisioning-Information-Edward-R-Tufte/dp/0961392118/ref=sr_1_8?ie=UTF8&qid=1376709039&sr=8-8&keywords=information+design)\r\n * Harvard's Data Viz Class [Harvard CS 171] (http://cs171.org)\r\n * Scott Murray's Tutorial on D3 [Scott Murray's Blog] (http://alignedleft.com/tutorials/)\r\n * Berkely's Viz Class [UC Berkeley] (http://vis.berkeley.edu/courses/cs294-10-sp11/wiki/index.php/CS294-10_Visualization)\r\n * Rice University's Data Viz class [Rice University] (http://had.co.nz/stat645/)\r\n\r\n* **Python** (Learning)\r\n * New To Python: [Learn Python the Hard Way](http://learnpythonthehardway.org/), [Google's Python Class](http://code.google.com/edu/languages/google-python-class/), [Think Python Downey, Allen B.] (http://www.greenteapress.com/thinkpython/)\r\n\r\n* **Python** (Libraries)\r\n * Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://www.lowindata.com/2013/installing-scientific-python-on-mac-os-x/)\r\n * [Data Science in iPython Notebooks](http://nborwankar.github.io/LearnDataScience/) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * Bayesian Inference | [pymc](https://github.com/pymc-devs/pymc)\r\n * Labeled data structures objects, statistical functions, etc [pandas](https://github.com/pydata/pandas) (See: Python for Data Analysis)\r\n * Python wrapper for the Twitter API [twython](https://github.com/ryanmcgrath/twython)\r\n * Tools for Data Mining & Analysis [scikit-learn](http://scikit-learn.org/stable/)\r\n * Network Modeling & Viz [networkx](http://networkx.github.io/)\r\n * Natural Language Toolkit [NLTK](http://nltk.org/)\r\n \r\n* **R** (Learning)\r\n * Software Design: The Art of R Programming [O'Reilly / Book](http://shop.oreilly.com/product/9781593273842.do)\r\n * R in a Nutshell [O'Reilly / Book](http://shop.oreilly.com/product/9780596801717.do)\r\n * Viz and Elegant Graphics in R: ggplot2 [Springer / Book](http://www.springer.com/statistics/computational+statistics/book/978-0-387-98140-6)\r\n * Machine Learning in R [Tutorial](http://blog.revolutionanalytics.com/2009/09/machine-learning-in-r-in-a-nutshell.html)\r\n * Guide to Getting Started in Machine Learning [Tutorial](http://abeautifulwww.com/2009/10/11/guide-to-getting-started-in-machine-learning/)\r\n * Learn R & Become a Data Analyst [Tutorial](https://www.datacamp.com/)\r\n\r\n* **R** (Libraries)\r\n * Natural Language Toolkit [OpenNLP](http://cran.r-project.org/web/packages/openNLP/index.html)\r\n * Text Mining [tm](http://cran.r-project.org/web/packages/tm/index.html]\r\n * Basic Viz [wordcloud](http://cran.r-project.org/web/packages/wordcloud/index.html)\r\n * Network Modeling & Viz [igraph](http://cran.r-project.org/web/packages/igraph/index.html)\r\n * Basic Machine Learning [e1071](http://cran.r-project.org/web/packages/e1071/index.html)\r\n * Kernel Method [kernlab](http://cran.r-project.org/web/packages/kernlab/index.html)\r\n * Chinese Language Processing [Rwordseg](http://jliblog.com/app/rwordseg)\r\n * Chinese Weibo Analysis [Rweibo](http://jliblog.com/app/rweibo)\r\n\r\n### Capstone Project\r\n* [Toy Data Ideas](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Capstone Analysis of Your Own Design; [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](https://www.coursolve.org/need/54)\r\n\r\n***\r\n### Further Study Resources:\r\n* [Coursera](http://coursera.org)\r\n* [Khan Academy](https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/term-life-insurance-and-death-probability)\r\n* [Metacademy](http://www.metacademy.org)\r\n* [Wolfram Alpha](http://www.wolframalpha.com/input/?i=torus)\r\n* [Wikipedia](http://en.wikipedia.org/wiki/List_of_cognitive_biases)\r\n* [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Kindle .mobis\r\n* Great PopSci Read: [The Signal and The Noise](http://www.amazon.com/Signal-Noise-Predictions-Fail-but-ebook/dp/B007V65R54/ref=tmm_kin_swatch_0?_encoding=UTF8&sr=8-1&qid=1376699450) Nate Silver\r\n* Zipfian Academy's [List of Resources](http://blog.zipfianacademy.com/post/46864003608/a-practical-intro-to-data-science)\r\n* [A Software Engineer's Guide to Getting Started w Data Science](http://www.rcasts.com/2012/12/software-engineers-guide-to-getting.html)\r\n* Data Scientist Interviews [Metamarkets](http://metamarkets.com/category/data-science/)\r\n\r\n### Sources for Datasets:\r\n* Competitions between Data Scientists on new datasets [Kaggle] (https://www.kaggle.com/)\r\n* Hilary Mason's List of Research-quality datasets [bit.ly](https://bitly.com/bundles/hmason/1)\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**, so I've explicitly marked when resources use other tools to teach conceptual material (like R)\r\n\r\n## Contribute\r\n\r\nPlease Share and Contribute Your Ideas -- **it's Open Source!**\r\n\r\nHere's [my transcript](https://github.com/datasciencemasters/go/wiki/%5BTranscript%5D-Clare-Corthell); Please **showcase your own** on the [wiki](https://github.com/datasciencemasters/go/wiki/)!\r\n\r\n[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)","google":"UA-43720588-1","note":"Don't delete this file! 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    The Open Source Data Science Masters

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    Curriculum

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    The Open Source Data Science Masters

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    Curriculum

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    + Project maintained by datasciencemasters + Hosted on GitHub Pages — Theme by mattgraham +
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    The Curriculum for learning Data Science, Open Source and at your fingertips.

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    Follow me on Twitter @clarecorthell

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    The Open Source Data Science Masters

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    Curriculum

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    The Open Source Data Science Masters

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    Curriculum

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    - Project maintained by datasciencemasters - Hosted on GitHub Pages — Theme by mattgraham -
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    This project is maintained by datasciencemasters

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    Follow me on Twitter @clarecorthell

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    NB: Fancy Homepage here (You are currently seeing the living document)

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    Follow me on Twitter @clarecorthell

    -The Open-Source Data Science Masters - Curriculum

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    The Internet is Your Oyster

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    - \ No newline at end of file + diff --git a/javascripts/scale.fix.js b/javascripts/scale.fix.js index 87a40ca7..08716c00 100644 --- a/javascripts/scale.fix.js +++ b/javascripts/scale.fix.js @@ -1,17 +1,20 @@ -var metas = document.getElementsByTagName('meta'); -var i; -if (navigator.userAgent.match(/iPhone/i)) { - for (i=0; i ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://blog.bright.com/2012/11/13/how-to-hire-a-data-scientist/) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n* **Intro to Data Science** [UW / Coursera](https://www.coursera.org/course/datasci)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n### Math\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://www.math.washington.edu/~burke/crs/407/lectures/)\r\n\r\n* **Statistics**\r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://greenteapress.com/thinkstats/) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Allen Downey / Book](http://www.greenteapress.com/thinkbayes/)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](https://www.coursera.org/course/algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](https://www.coursera.org/course/datasci)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](https://www.udacity.com/course/ud617) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * SQL Tutorial [w3schools / Tutorials](http://www.w3schools.com/sql/)\r\n * SQL Tutorial [SQLZOO / Tutorials](http://sqlzoo.net/wiki/Main_Page)\r\n * Introduction to Databases [Stanford / Online Course](http://class2go.stanford.edu/db/Winter2013/)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Online Book](http://i.stanford.edu/~ullman/mmds.html)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval [Stanford / Book](http://nlp.stanford.edu/IR-book/information-retrieval-book.html)\r\n\r\n* **Machine Learning**\r\n * Machine Learning [Ng Stanford / Coursera](https://www.coursera.org/course/ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * The Elements of Statistical Learning [Stanford / Digital Book](http://www-stat.stanford.edu/~tibs/ElemStatLearn/)^\r\n * Machine Learning [Caltech / Edx](http://work.caltech.edu/lectures.html)\r\n * Neural Networks [U Toronto / Coursera](https://class.coursera.org/neuralnets-2012-001)\r\n\r\n* **Statistical Network Analysis & Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)\r\n * Probabalistic Graphical Models [Stanford / Coursera](https://www.coursera.org/course/pgm)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python (NLTK library) [Digital Book](http://nltk.org/book/), [Paper Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://blogs.ischool.berkeley.edu/i290-abdt-s12/)\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](https://www.coursera.org/course/networksonline)\r\n\r\n* **Visualization**\r\n * Envisioning Information (Information Visualization) [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * Data Visualization, CS 171 [Harvard / Lectures](http://cm.dce.harvard.edu/2014/02/24028/publicationListing.shtml)\r\n * Scott Murray's Tutorial on D3 [Blog / Tutorials](http://alignedleft.com/tutorials/)\r\n * Berkely's Viz Class [UC Berkeley / Course Docs](http://vis.berkeley.edu/courses/cs294-10-sp11/wiki/index.php/CS294-10_Visualization)\r\n * Rice University's Data Viz class [Rice University](http://had.co.nz/stat645/)\r\n\r\n* **Python** (Learning)\r\n * Learn Python the Hard Way [eBook](http://learnpythonthehardway.org/)\r\n * Python [Class / Google](http://code.google.com/edu/languages/google-python-class/)\r\n * [Think Python](http://www.greenteapress.com/thinkpython/)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-00sc-introduction-to-computer-science-and-programming-spring-2011/)\r\n\r\n* **Python** (Libraries)\r\n * Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://www.lowindata.com/2013/installing-scientific-python-on-mac-os-x/)\r\n * [Data Science in iPython Notebooks](http://nborwankar.github.io/LearnDataScience/) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * Bayesian Inference | [pymc](https://github.com/pymc-devs/pymc)\r\n * Labeled data structures objects, statistical functions, etc [pandas](https://github.com/pydata/pandas) (See: Python for Data Analysis)\r\n * Python wrapper for the Twitter API [twython](https://github.com/ryanmcgrath/twython)\r\n * Tools for Data Mining & Analysis [scikit-learn](http://scikit-learn.org/stable/)\r\n * Network Modeling & Viz [networkx](http://networkx.github.io/)\r\n * Natural Language Toolkit [NLTK](http://nltk.org/)\r\n\r\n### R resources are now [here](https://github.com/datasciencemasters/go/blob/master/r-resources.md) (Please help!)\r\n\r\n### Capstone Project\r\n* [Toy Data Ideas](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Capstone Analysis of Your Own Design; [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](https://www.coursolve.org/need/54)\r\n\r\n***\r\n### Further Study Resources:\r\n* [Coursera](http://coursera.org)\r\n* [Khan Academy](https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/term-life-insurance-and-death-probability)\r\n* [Metacademy](http://www.metacademy.org)\r\n* [Wolfram Alpha](http://www.wolframalpha.com/input/?i=torus)\r\n* [Wikipedia](http://en.wikipedia.org/wiki/List_of_cognitive_biases)\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$```](http://amzn.to/1hoxQoG)\r\n* Zipfian Academy's [List of Resources](http://blog.zipfianacademy.com/post/46864003608/a-practical-intro-to-data-science)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://www.rcasts.com/2012/12/software-engineers-guide-to-getting.html)\r\n* Data Scientist Interviews [Metamarkets](http://metamarkets.com/category/data-science/)\r\n* /r/MachineLearning [Reddit](http://www.reddit.com/r/machinelearning)\r\n\r\n### Sources for Datasets:\r\n* [NIPS Feature Selection](http://www.nipsfsc.ecs.soton.ac.uk/datasets/)\r\n* [Stanford Network Analysis Project](https://snap.stanford.edu/)\r\n* Data Science Contests [Kaggle] (https://www.kaggle.com/)\r\n* [@hmason's](https://twitter.com/hmason) curated dataset list [bit.ly](https://bitly.com/bundles/hmason/1)\r\n* Classical Datasets for Your Specific Need [UCI Machine Learning Repository Datasets](\r\nhttp://archive.ics.uci.edu/ml/datasets.html)\r\n* [Time Series Data Library](http://datamarket.com/data/list/?q=provider:tsdl)\r\n* USA Congressional Voting Records [Voteview](http://voteview.org/downloads.asp)\r\n* [Qandl](http://www.quandl.com) provides a lot of interesting data with a clean API.\r\n\r\n### Notation\r\nPaid books, courses, and resources are noted with ```$```.\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**, so I've explicitly marked when resources use other tools to teach conceptual material (like R)\r\n\r\n## Contribute\r\n\r\nPlease Share and Contribute Your Ideas -- **it's Open Source!**\r\n\r\nHere's [my transcript](https://github.com/datasciencemasters/go/wiki/%5BTranscript%5D-Clare-Corthell).\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request with your name! eg ```clare-corthell-transcript.md```\r\n\r\n[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The Open Source Data Science Masters","tagline":"Curriculum","body":"[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\n### The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*see [my transcript](http://bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://blog.bright.com/2012/11/13/how-to-hire-a-data-scientist/) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n* **Intro to Data Science** [UW / Coursera](https://www.coursera.org/course/datasci)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n### Math\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://www.math.washington.edu/~burke/crs/407/lectures/)\r\n\r\n* **Statistics**\r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://greenteapress.com/thinkstats/) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Allen Downey / Book](http://www.greenteapress.com/thinkbayes/)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](https://www.coursera.org/course/algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](https://www.coursera.org/course/datasci)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](https://www.udacity.com/course/ud617) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * SQL Tutorial [w3schools / Tutorials](http://www.w3schools.com/sql/)\r\n * SQL Tutorial [SQLZOO / Tutorials](http://sqlzoo.net/wiki/Main_Page)\r\n * Introduction to Databases [Stanford / Online Course](http://class2go.stanford.edu/db/Winter2013/)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Online Book](http://i.stanford.edu/~ullman/mmds.html)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval [Stanford / Book](http://nlp.stanford.edu/IR-book/information-retrieval-book.html)\r\n\r\n* **Machine Learning**\r\n * Machine Learning [Ng Stanford / Coursera](https://www.coursera.org/course/ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * The Elements of Statistical Learning [Stanford / Digital Book](http://www-stat.stanford.edu/~tibs/ElemStatLearn/)^\r\n * Machine Learning [Caltech / Edx](http://work.caltech.edu/lectures.html)\r\n * Neural Networks [U Toronto / Coursera](https://class.coursera.org/neuralnets-2012-001)\r\n\r\n* **Statistical Network Analysis & Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)\r\n * Probabalistic Graphical Models [Stanford / Coursera](https://www.coursera.org/course/pgm)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python (NLTK library) [Digital Book](http://nltk.org/book/), [Paper Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://blogs.ischool.berkeley.edu/i290-abdt-s12/)\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](https://www.coursera.org/course/networksonline)\r\n\r\n* **Visualization**\r\n * Envisioning Information (Information Visualization) [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * Data Visualization, CS 171 [Harvard / Lectures](http://cm.dce.harvard.edu/2014/02/24028/publicationListing.shtml)\r\n * Scott Murray's Tutorial on D3 [Blog / Tutorials](http://alignedleft.com/tutorials/)\r\n * Berkely's Viz Class [UC Berkeley / Course Docs](http://vis.berkeley.edu/courses/cs294-10-sp11/wiki/index.php/CS294-10_Visualization)\r\n * Rice University's Data Viz class [Rice University](http://had.co.nz/stat645/)\r\n\r\n* **Python** (Learning)\r\n * Learn Python the Hard Way [eBook](http://learnpythonthehardway.org/)\r\n * Python [Class / Google](http://code.google.com/edu/languages/google-python-class/)\r\n * [Think Python](http://www.greenteapress.com/thinkpython/)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-00sc-introduction-to-computer-science-and-programming-spring-2011/)\r\n\r\n* **Python** (Libraries)\r\n * Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://www.lowindata.com/2013/installing-scientific-python-on-mac-os-x/)\r\n * [Data Science in iPython Notebooks](http://nborwankar.github.io/LearnDataScience/) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * Bayesian Inference | [pymc](https://github.com/pymc-devs/pymc)\r\n * Labeled data structures objects, statistical functions, etc [pandas](https://github.com/pydata/pandas) (See: Python for Data Analysis)\r\n * Python wrapper for the Twitter API [twython](https://github.com/ryanmcgrath/twython)\r\n * Tools for Data Mining & Analysis [scikit-learn](http://scikit-learn.org/stable/)\r\n * Network Modeling & Viz [networkx](http://networkx.github.io/)\r\n * Natural Language Toolkit [NLTK](http://nltk.org/)\r\n\r\n### R resources are now [here](https://github.com/datasciencemasters/go/blob/master/r-resources.md) (Please help!)\r\n\r\n### Capstone Project\r\n* [Toy Data Ideas](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Capstone Analysis of Your Own Design; [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](https://www.coursolve.org/need/54)\r\n\r\n***\r\n### Further Study Resources:\r\n* [Coursera](http://coursera.org)\r\n* [Khan Academy](https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/term-life-insurance-and-death-probability)\r\n* [Metacademy](http://www.metacademy.org)\r\n* [Wolfram Alpha](http://www.wolframalpha.com/input/?i=torus)\r\n* [Wikipedia](http://en.wikipedia.org/wiki/List_of_cognitive_biases)\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$```](http://amzn.to/1hoxQoG)\r\n* Zipfian Academy's [List of Resources](http://blog.zipfianacademy.com/post/46864003608/a-practical-intro-to-data-science)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://www.rcasts.com/2012/12/software-engineers-guide-to-getting.html)\r\n* Data Scientist Interviews [Metamarkets](http://metamarkets.com/category/data-science/)\r\n* /r/MachineLearning [Reddit](http://www.reddit.com/r/machinelearning)\r\n\r\n### Sources for Datasets:\r\n* [NIPS Feature Selection](http://www.nipsfsc.ecs.soton.ac.uk/datasets/)\r\n* [Stanford Network Analysis Project](https://snap.stanford.edu/)\r\n* Data Science Contests [Kaggle] (https://www.kaggle.com/)\r\n* [@hmason's](https://twitter.com/hmason) curated dataset list [bit.ly](https://bitly.com/bundles/hmason/1)\r\n* Classical Datasets for Your Specific Need [UCI Machine Learning Repository Datasets](\r\nhttp://archive.ics.uci.edu/ml/datasets.html)\r\n* [Time Series Data Library](http://datamarket.com/data/list/?q=provider:tsdl)\r\n* USA Congressional Voting Records [Voteview](http://voteview.org/downloads.asp)\r\n* [Qandl](http://www.quandl.com) provides a lot of interesting data with a clean API.\r\n\r\n### Notation\r\nPaid books, courses, and resources are noted with ```$```.\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**, so I've explicitly marked when resources use other tools to teach conceptual material (like R)\r\n\r\n## Contribute\r\n\r\nPlease Share and Contribute Your Ideas -- **it's Open Source!**\r\n\r\nHere's [my transcript](https://github.com/datasciencemasters/go/wiki/%5BTranscript%5D-Clare-Corthell).\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request with your name! eg ```clare-corthell-transcript.md```\r\n\r\n[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)\r\n","google":"UA-43720588-1","note":"Don't delete this file! 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-} - -@font-face { - font-family: 'OpenSansSemibold'; - src: url("../fonts/OpenSans-Semibold-webfont.eot"); - src: url("../fonts/OpenSans-Semibold-webfont.eot?#iefix") format("embedded-opentype"), url("../fonts/OpenSans-Semibold-webfont.woff") format("woff"), url("../fonts/OpenSans-Semibold-webfont.ttf") format("truetype"), url("../fonts/OpenSans-Semibold-webfont.svg#OpenSansSemibold") format("svg"); - font-weight: normal; - font-style: normal; - -webkit-font-smoothing: antialiased; -} - -@font-face { - font-family: 'OpenSansSemiboldItalic'; - src: url("../fonts/OpenSans-SemiboldItalic-webfont.eot"); - src: url("../fonts/OpenSans-SemiboldItalic-webfont.eot?#iefix") format("embedded-opentype"), url("../fonts/OpenSans-SemiboldItalic-webfont.woff") format("woff"), url("../fonts/OpenSans-SemiboldItalic-webfont.ttf") format("truetype"), url("../fonts/OpenSans-SemiboldItalic-webfont.svg#OpenSansSemiboldItalic") format("svg"); - font-weight: normal; - font-style: normal; - -webkit-font-smoothing: antialiased; -} - -@font-face { - font-family: 'OpenSansBold'; - src: url("../fonts/OpenSans-Bold-webfont.eot"); - src: url("../fonts/OpenSans-Bold-webfont.eot?#iefix") format("embedded-opentype"), url("../fonts/OpenSans-Bold-webfont.woff") format("woff"), url("../fonts/OpenSans-Bold-webfont.ttf") format("truetype"), url("../fonts/OpenSans-Bold-webfont.svg#OpenSansBold") format("svg"); - font-weight: normal; - font-style: normal; - -webkit-font-smoothing: antialiased; -} - -@font-face { - font-family: 'OpenSansBoldItalic'; - src: url("../fonts/OpenSans-BoldItalic-webfont.eot"); - src: url("../fonts/OpenSans-BoldItalic-webfont.eot?#iefix") format("embedded-opentype"), url("../fonts/OpenSans-BoldItalic-webfont.woff") format("woff"), url("../fonts/OpenSans-BoldItalic-webfont.ttf") format("truetype"), url("../fonts/OpenSans-BoldItalic-webfont.svg#OpenSansBoldItalic") format("svg"); - font-weight: normal; - font-style: normal; - -webkit-font-smoothing: antialiased; -} - -/* normalize.css 2012-02-07T12:37 UTC - http://github.com/necolas/normalize.css */ -/* ============================================================================= - HTML5 display definitions - ========================================================================== */ -/* - * Corrects block display not defined in IE6/7/8/9 & FF3 - */ -article, -aside, -details, -figcaption, -figure, -footer, -header, -hgroup, -nav, -section, -summary { - display: block; -} - -/* - * Corrects inline-block display not defined in IE6/7/8/9 & FF3 - */ -audio, -canvas, -video { - display: inline-block; - *display: inline; - *zoom: 1; -} - -/* - * Prevents modern browsers from displaying 'audio' without controls - */ -audio:not([controls]) { - display: none; -} - -/* - * Addresses styling for 'hidden' attribute not present in IE7/8/9, FF3, S4 - * Known issue: no IE6 support - */ -[hidden] { - display: none; -} - -/* ============================================================================= - Base - ========================================================================== */ -/* - * 1. Corrects text resizing oddly in IE6/7 when body font-size is set using em units - * http://clagnut.com/blog/348/#c790 - * 2. Prevents iOS text size adjust after orientation change, without disabling user zoom - * www.456bereastreet.com/archive/201012/controlling_text_size_in_safari_for_ios_without_disabling_user_zoom/ - */ -html { - font-size: 100%; - /* 1 */ - -webkit-text-size-adjust: 100%; - /* 2 */ - -ms-text-size-adjust: 100%; - /* 2 */ -} - -/* - * Addresses font-family inconsistency between 'textarea' and other form elements. - */ -html, -button, -input, -select, -textarea { - font-family: sans-serif; -} - -/* - * Addresses margins handled incorrectly in IE6/7 - */ -body { +@import url(https://fonts.googleapis.com/css?family=Arvo:400,700,400italic); + +/* MeyerWeb Reset */ + +html, body, div, span, applet, object, iframe, +h1, h2, h3, h4, h5, h6, p, blockquote, pre, +a, abbr, acronym, address, big, cite, code, +del, dfn, em, img, ins, kbd, q, s, samp, +small, strike, strong, sub, sup, tt, var, +b, u, i, center, +dl, dt, dd, ol, ul, li, +fieldset, form, label, legend, +table, caption, tbody, tfoot, thead, tr, th, td, +article, aside, canvas, details, embed, +figure, figcaption, footer, header, hgroup, +menu, nav, output, ruby, section, summary, +time, mark, audio, video { margin: 0; + padding: 0; + border: 0; + font: inherit; + vertical-align: baseline; } -/* ============================================================================= - Links - ========================================================================== */ -/* - * Addresses outline displayed oddly in Chrome - */ -a:focus { - outline: thin dotted; -} - -/* - * Improves readability when focused and also mouse hovered in all browsers - * people.opera.com/patrickl/experiments/keyboard/test - */ -a:hover, -a:active { - outline: 0; -} - -/* ============================================================================= - Typography - ========================================================================== */ -/* - * Addresses font sizes and margins set differently in IE6/7 - * Addresses font sizes within 'section' and 'article' in FF4+, Chrome, S5 - */ -h1 { - font-size: 2em; - margin: 0.67em 0; -} -h2 { - font-size: 1.5em; - margin: 0.83em 0; -} +/* Base text styles */ + +body { + padding:10px 50px 0 0; + font-family:"Helvetica Neue", Helvetica, Arial, sans-serif; + font-size: 14px; + color: #232323; + background-color: #FBFAF7; + margin: 0; + line-height: 1.8em; + -webkit-font-smoothing: antialiased; -h3 { - font-size: 1.17em; - margin: 1em 0; } -h4 { - font-size: 1em; - margin: 1.33em 0; +h1, h2, h3, h4, h5, h6 { + color:#232323; + margin:36px 0 10px; } -h5 { - font-size: 0.83em; - margin: 1.67em 0; +p, ul, ol, table, dl { + margin:0 0 22px; } -h6 { - font-size: 0.75em; - margin: 2.33em 0; +h1, h2, h3 { + font-family: Arvo, Monaco, serif; + line-height:1.3; + font-weight: normal; } -/* - * Addresses styling not present in IE7/8/9, S5, Chrome - */ -abbr[title] { - border-bottom: 1px dotted; +h1,h2, h3 { + display: block; + border-bottom: 1px solid #ccc; + padding-bottom: 5px; } -/* - * Addresses style set to 'bolder' in FF3+, S4/5, Chrome -*/ -b, -strong { - font-weight: bold; +h1 { + font-size: 30px; } -blockquote { - margin: 1em 40px; +h2 { + font-size: 24px; } -/* - * Addresses styling not present in S5, Chrome - */ -dfn { - font-style: italic; +h3 { + font-size: 18px; } -/* - * Addresses styling not present in IE6/7/8/9 - */ -mark { - background: #ff0; - color: #000; +h4, h5, h6 { + font-family: Arvo, Monaco, serif; + font-weight: 700; } -/* - * Addresses margins set differently in IE6/7 - */ -p, -pre { - margin: 1em 0; -} - -/* - * Corrects font family set oddly in IE6, S4/5, Chrome - * en.wikipedia.org/wiki/User:Davidgothberg/Test59 - */ -pre, -code, -kbd, -samp { - font-family: monospace, serif; - _font-family: 'courier new', monospace; - font-size: 1em; -} - -/* - * 1. Addresses CSS quotes not supported in IE6/7 - * 2. Addresses quote property not supported in S4 - */ -/* 1 */ -q { - quotes: none; -} - -/* 2 */ -q:before, -q:after { - content: ''; - content: none; -} - -small { - font-size: 75%; -} - -/* - * Prevents sub and sup affecting line-height in all browsers - * gist.github.com/413930 - */ -sub, -sup { - font-size: 75%; - line-height: 0; - position: relative; - vertical-align: baseline; +a { + color:#C30000; + font-weight:200; + text-decoration:none; } -sup { - top: -0.5em; +a:hover { + text-decoration: underline; } -sub { - bottom: -0.25em; +a small { + font-size: 12px; } -/* ============================================================================= - Lists - ========================================================================== */ -/* - * Addresses margins set differently in IE6/7 - */ -dl, -menu, -ol, -ul { - margin: 1em 0; +em { + font-style: italic; } -dd { - margin: 0 0 0 40px; +strong { + font-weight:700; } -/* - * Addresses paddings set differently in IE6/7 - */ -menu, -ol, ul { - padding: 0 0 0 40px; -} - -/* - * Corrects list images handled incorrectly in IE7 - */ -nav ul, -nav ol { - list-style: none; - list-style-image: none; -} - -/* ============================================================================= - Embedded content - ========================================================================== */ -/* - * 1. Removes border when inside 'a' element in IE6/7/8/9, FF3 - * 2. Improves image quality when scaled in IE7 - * code.flickr.com/blog/2008/11/12/on-ui-quality-the-little-things-client-side-image-resizing/ - */ -img { - border: 0; - /* 1 */ - -ms-interpolation-mode: bicubic; - /* 2 */ + list-style: inside; + padding-left: 25px; } -/* - * Corrects overflow displayed oddly in IE9 - */ -svg:not(:root) { - overflow: hidden; +ol { + list-style: decimal inside; + padding-left: 20px; } -/* ============================================================================= - Figures - ========================================================================== */ -/* - * Addresses margin not present in IE6/7/8/9, S5, O11 - */ -figure { +blockquote { margin: 0; + padding: 0 0 0 20px; + font-style: italic; } -/* ============================================================================= - Forms - ========================================================================== */ -/* - * Corrects margin displayed oddly in IE6/7 - */ -form { - margin: 0; +dl, dt, dd, dl p { + font-color: #444; } -/* - * Define consistent border, margin, and padding - */ -fieldset { - border: 1px solid #c0c0c0; - margin: 0 2px; - padding: 0.35em 0.625em 0.75em; +dl dt { + font-weight: bold; } -/* - * 1. Corrects color not being inherited in IE6/7/8/9 - * 2. Corrects text not wrapping in FF3 - * 3. Corrects alignment displayed oddly in IE6/7 - */ -legend { - border: 0; - /* 1 */ - padding: 0; - white-space: normal; - /* 2 */ - *margin-left: -7px; - /* 3 */ -} - -/* - * 1. Corrects font size not being inherited in all browsers - * 2. Addresses margins set differently in IE6/7, FF3+, S5, Chrome - * 3. Improves appearance and consistency in all browsers - */ -button, -input, -select, -textarea { - font-size: 100%; - /* 1 */ - margin: 0; - /* 2 */ - vertical-align: baseline; - /* 3 */ - *vertical-align: middle; - /* 3 */ -} - -/* - * Addresses FF3/4 setting line-height on 'input' using !important in the UA stylesheet - */ -button, -input { - line-height: normal; - /* 1 */ -} - -/* - * 1. Improves usability and consistency of cursor style between image-type 'input' and others - * 2. Corrects inability to style clickable 'input' types in iOS - * 3. Removes inner spacing in IE7 without affecting normal text inputs - * Known issue: inner spacing remains in IE6 - */ -button, -input[type="button"], -input[type="reset"], -input[type="submit"] { - cursor: pointer; - /* 1 */ - -webkit-appearance: button; - /* 2 */ - *overflow: visible; - /* 3 */ -} - -/* - * Re-set default cursor for disabled elements - */ -button[disabled], -input[disabled] { - cursor: default; -} - -/* - * 1. Addresses box sizing set to content-box in IE8/9 - * 2. Removes excess padding in IE8/9 - * 3. Removes excess padding in IE7 - Known issue: excess padding remains in IE6 - */ -input[type="checkbox"], -input[type="radio"] { - box-sizing: border-box; - /* 1 */ - padding: 0; - /* 2 */ - *height: 13px; - /* 3 */ - *width: 13px; - /* 3 */ -} - -/* - * 1. Addresses appearance set to searchfield in S5, Chrome - * 2. Addresses box-sizing set to border-box in S5, Chrome (include -moz to future-proof) - */ -input[type="search"] { - -webkit-appearance: textfield; - /* 1 */ - -moz-box-sizing: content-box; - -webkit-box-sizing: content-box; - /* 2 */ - box-sizing: content-box; -} - -/* - * Removes inner padding and search cancel button in S5, Chrome on OS X - */ -input[type="search"]::-webkit-search-decoration, -input[type="search"]::-webkit-search-cancel-button { - -webkit-appearance: none; -} - -/* - * Removes inner padding and border in FF3+ - * www.sitepen.com/blog/2008/05/14/the-devils-in-the-details-fixing-dojos-toolbar-buttons/ - */ -button::-moz-focus-inner, -input::-moz-focus-inner { - border: 0; - padding: 0; +dl dd { + padding-left: 20px; + font-style: italic; } -/* - * 1. Removes default vertical scrollbar in IE6/7/8/9 - * 2. Improves readability and alignment in all browsers - */ -textarea { - overflow: auto; - /* 1 */ - vertical-align: top; - /* 2 */ +dl p { + padding-left: 20px; + font-style: italic; } -/* ============================================================================= - Tables - ========================================================================== */ -/* - * Remove most spacing between table cells - */ -table { - border-collapse: collapse; - border-spacing: 0; +hr { + border:0; + background:#ccc; + height:1px; + margin:0 0 24px; } -body { - padding: 0px 0 20px 0px; - margin: 0px; - font: 14px/1.5 "OpenSansRegular", "Helvetica Neue", Helvetica, Arial, sans-serif; - color: #f0e7d5; - font-weight: normal; - background: #252525; - background-attachment: fixed !important; - background: -webkit-gradient(linear, 50% 0%, 50% 100%, color-stop(0%, #2a2a29), color-stop(100%, #1c1c1c)); - background: -webkit-linear-gradient(#2a2a29, #1c1c1c); - background: -moz-linear-gradient(#2a2a29, #1c1c1c); - background: -o-linear-gradient(#2a2a29, #1c1c1c); - background: -ms-linear-gradient(#2a2a29, #1c1c1c); - background: linear-gradient(#2a2a29, #1c1c1c); -} +/* Images */ -h1, h2, h3, h4, h5, h6 { - color: #e8e8e8; - margin: 0 0 10px; - font-family: 'OpenSansRegular', "Helvetica Neue", Helvetica, Arial, sans-serif; - font-weight: normal; +img { + position: relative; + margin: 0 auto; + max-width: 650px; + padding: 5px; + margin: 10px 0 32px 0; + border: 1px solid #ccc; } -p, ul, ol, table, pre, dl { - margin: 0 0 20px; +p img { + display: inline; + margin: 0; + padding: 0; + vertical-align: middle; + text-align: center; + border: none; } -h1, h2, h3 { - line-height: 1.1; -} +/* Code blocks */ -h1 { - font-size: 28px; +code, pre { + font-family: Monaco, "Bitstream Vera Sans Mono", "Lucida Console", Terminal, monospace; + color:#000; + font-size:14px; } -h2 { - font-size: 24px; +pre { + padding: 4px 12px; + background: #FDFEFB; + border-radius:4px; + border:1px solid #D7D8C8; + overflow: auto; + overflow-y: hidden; + margin-bottom: 32px; } -h4, h5, h6 { - color: #e8e8e8; -} -h3 { - font-size: 18px; - line-height: 24px; - font-family: 'OpenSansRegular', "Helvetica Neue", Helvetica, Arial, sans-serif !important; - font-weight: normal; - color: #b6b6b6; -} +/* Tables */ -a { - color: #ffcc00; - font-weight: 400; - text-decoration: none; -} -a:hover { - color: #ffeb9b; +table { + width:100%; } -a small { - font-size: 11px; - color: #666; - margin-top: -0.6em; - display: block; -} +table { + border: 1px solid #ccc; + margin-bottom: 32px; + text-align: left; + } -ul { - list-style-image: url("../images/bullet.png"); -} +th { + font-family: 'Arvo', Helvetica, Arial, sans-serif; + font-size: 18px; + font-weight: normal; + padding: 10px; + background: #232323; + color: #FDFEFB; + } + +td { + padding: 10px; + background: #ccc; + } -strong { - font-family: 'OpenSansBold', "Helvetica Neue", Helvetica, Arial, sans-serif !important; - font-weight: normal; -} +/* Wrapper */ .wrapper { - max-width: 650px; - margin: 0 auto; - position: relative; - padding: 0 20px; + width:960px; } -section img { - max-width: 100%; -} -blockquote { - border-left: 3px solid #ffcc00; - margin: 0; - padding: 0 0 0 20px; - font-style: italic; +/* Header */ + +header { + background-color: #171717; + color: #FDFDFB; + width:170px; + float:left; + position:fixed; + border: 1px solid #000; + -webkit-border-top-right-radius: 4px; + -webkit-border-bottom-right-radius: 4px; + -moz-border-radius-topright: 4px; + -moz-border-radius-bottomright: 4px; + border-top-right-radius: 4px; + border-bottom-right-radius: 4px; + padding: 34px 25px 22px 50px; + margin: 30px 25px 0 0; + -webkit-font-smoothing: antialiased; } -code { - font-family: "Lucida Sans", Monaco, Bitstream Vera Sans Mono, Lucida Console, Terminal; - color: #efefef; - font-size: 13px; - margin: 0 4px; - padding: 4px 6px; - -moz-border-radius: 2px; - -webkit-border-radius: 2px; - -o-border-radius: 2px; - -ms-border-radius: 2px; - -khtml-border-radius: 2px; - border-radius: 2px; +p.header { + font-size: 16px; } -pre { - padding: 8px 15px; - background: #191919; - -moz-border-radius: 2px; - -webkit-border-radius: 2px; - -o-border-radius: 2px; - -ms-border-radius: 2px; - -khtml-border-radius: 2px; - border-radius: 2px; - border: 1px solid #121212; - -moz-box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.3); - -webkit-box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.3); - -o-box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.3); - box-shadow: inset 0 1px 3px rgba(0, 0, 0, 0.3); - overflow: auto; - overflow-y: hidden; +h1.header { + font-family: Arvo, sans-serif; + font-size: 30px; + font-weight: 300; + line-height: 1.3em; + border-bottom: none; + margin-top: 0; } -pre code { - color: #efefef; - text-shadow: 0px 1px 0px #000; - margin: 0; - padding: 0; + + +h1.header, a.header, a.name, header a{ + color: #fff; } -table { - width: 100%; - border-collapse: collapse; +a.header { + text-decoration: underline; } -th { - text-align: left; - padding: 5px 10px; - border-bottom: 1px solid #434343; - color: #b6b6b6; - font-family: 'OpenSansSemibold', "Helvetica Neue", Helvetica, Arial, sans-serif !important; - font-weight: normal; +a.name { + white-space: nowrap; } -td { - text-align: left; - padding: 5px 10px; - border-bottom: 1px solid #434343; +header ul { + list-style:none; + padding:0; } -hr { - border: 0; - outline: none; - height: 3px; - background: transparent url("../images/hr.gif") center center repeat-x; - margin: 0 0 20px; -} - -dt { - color: #F0E7D5; - font-family: 'OpenSansSemibold', "Helvetica Neue", Helvetica, Arial, sans-serif !important; - font-weight: normal; -} - -#header { - z-index: 100; - left: 0; - top: 0px; - height: 60px; - width: 100%; - position: fixed; - background: url(../images/nav-bg.gif) #353535; - border-bottom: 4px solid #434343; - -moz-box-shadow: 0px 1px 3px rgba(0, 0, 0, 0.25); - -webkit-box-shadow: 0px 1px 3px rgba(0, 0, 0, 0.25); - -o-box-shadow: 0px 1px 3px rgba(0, 0, 0, 0.25); - box-shadow: 0px 1px 3px rgba(0, 0, 0, 0.25); -} -#header nav { - max-width: 650px; - margin: 0 auto; - padding: 0 10px; - background: blue; - margin: 6px auto; -} -#header nav li { - font-family: 'OpenSansLight', "Helvetica Neue", Helvetica, Arial, sans-serif; - font-weight: normal; - list-style: none; - display: inline; - color: white; - line-height: 50px; - text-shadow: 0px 1px 0px rgba(0, 0, 0, 0.2); - font-size: 14px; -} -#header nav li a { - color: white; - border: 1px solid #5d910b; - background: -webkit-gradient(linear, 50% 0%, 50% 100%, color-stop(0%, #93bd20), color-stop(100%, #659e10)); - background: -webkit-linear-gradient(#93bd20, #659e10); - background: -moz-linear-gradient(#93bd20, #659e10); - background: -o-linear-gradient(#93bd20, #659e10); - background: -ms-linear-gradient(#93bd20, #659e10); - background: linear-gradient(#93bd20, #659e10); - -moz-border-radius: 2px; - -webkit-border-radius: 2px; - -o-border-radius: 2px; - -ms-border-radius: 2px; - -khtml-border-radius: 2px; - border-radius: 2px; - -moz-box-shadow: inset 0px 1px 0px rgba(255, 255, 255, 0.3), 0px 3px 7px rgba(0, 0, 0, 0.7); - -webkit-box-shadow: inset 0px 1px 0px rgba(255, 255, 255, 0.3), 0px 3px 7px rgba(0, 0, 0, 0.7); - -o-box-shadow: inset 0px 1px 0px rgba(255, 255, 255, 0.3), 0px 3px 7px rgba(0, 0, 0, 0.7); - box-shadow: inset 0px 1px 0px rgba(255, 255, 255, 0.3), 0px 3px 7px rgba(0, 0, 0, 0.7); - background-color: #93bd20; - padding: 10px 12px; - margin-top: 6px; - line-height: 14px; - font-size: 14px; - display: inline-block; - text-align: center; +header li { + list-style-type: none; + width:132px; + height:15px; + margin-bottom: 12px; + line-height: 1em; + padding: 6px 6px 6px 7px; + + background: #AF0011; + background: -moz-linear-gradient(top, #AF0011 0%, #820011 100%); + background: -webkit-gradient(linear, left top, left bottom, color-stop(0%,#f8f8f8), color-stop(100%,#dddddd)); + background: -webkit-linear-gradient(top, #AF0011 0%,#820011 100%); + background: -o-linear-gradient(top, #AF0011 0%,#820011 100%); + background: -ms-linear-gradient(top, #AF0011 0%,#820011 100%); + background: linear-gradient(top, #AF0011 0%,#820011 100%); + + border-radius:4px; + border:1px solid #0D0D0D; + + -webkit-box-shadow: inset 0px 1px 1px 0 rgba(233,2,38, 1); + box-shadow: inset 0px 1px 1px 0 rgba(233,2,38, 1); + } -#header nav li a:hover { - background: -webkit-gradient(linear, 50% 0%, 50% 100%, color-stop(0%, #749619), color-stop(100%, #527f0e)); - background: -webkit-linear-gradient(#749619, #527f0e); - background: -moz-linear-gradient(#749619, #527f0e); - background: -o-linear-gradient(#749619, #527f0e); - background: -ms-linear-gradient(#749619, #527f0e); - background: linear-gradient(#749619, #527f0e); - background-color: #659e10; - border: 1px solid #527f0e; - -moz-box-shadow: inset 0px 1px 1px rgba(0, 0, 0, 0.2), 0px 1px 0px rgba(0, 0, 0, 0); - -webkit-box-shadow: inset 0px 1px 1px rgba(0, 0, 0, 0.2), 0px 1px 0px rgba(0, 0, 0, 0); - -o-box-shadow: inset 0px 1px 1px rgba(0, 0, 0, 0.2), 0px 1px 0px rgba(0, 0, 0, 0); - box-shadow: inset 0px 1px 1px rgba(0, 0, 0, 0.2), 0px 1px 0px rgba(0, 0, 0, 0); -} -#header nav li.fork { - float: left; - margin-left: 0px; -} -#header nav li.downloads { - float: right; - margin-left: 6px; -} -#header nav li.title { - float: right; - margin-right: 10px; - font-size: 11px; + +header li:hover { + background: #C3001D; + background: -moz-linear-gradient(top, #C3001D 0%, #950119 100%); + background: -webkit-gradient(linear, left top, left bottom, color-stop(0%,#f8f8f8), color-stop(100%,#dddddd)); + background: -webkit-linear-gradient(top, #C3001D 0%,#950119 100%); + background: -o-linear-gradient(top, #C3001D 0%,#950119 100%); + background: -ms-linear-gradient(top, #C3001D 0%,#950119 100%); + background: linear-gradient(top, #C3001D 0%,#950119 100%); } -section { - max-width: 650px; - padding: 30px 0px 50px 0px; - margin: 20px 0; - margin-top: 70px; +a.buttons { + -webkit-font-smoothing: antialiased; + background: url(../images/arrow-down.png) no-repeat; + font-weight: normal; + text-shadow: rgba(0, 0, 0, 0.4) 0 -1px 0; + padding: 2px 2px 2px 22px; + height: 30px; } -section #title { - border: 0; - outline: none; - margin: 0 0 50px 0; - padding: 0 0 5px 0; -} -section #title h1 { - font-family: 'OpenSansLight', "Helvetica Neue", Helvetica, Arial, sans-serif; - font-weight: normal; - font-size: 40px; - text-align: center; - line-height: 36px; + +a.github { + background: url(../images/octocat-small.png) no-repeat 1px; } -section #title p { - color: #d7cfbe; - font-family: 'OpenSansLight', "Helvetica Neue", Helvetica, Arial, sans-serif; - font-weight: normal; - font-size: 18px; - text-align: center; + +a.buttons:hover { + color: #fff; + text-decoration: none; } -section #title .credits { - font-size: 11px; - font-family: 'OpenSansRegular', "Helvetica Neue", Helvetica, Arial, sans-serif; - font-weight: normal; - color: #696969; - margin-top: -10px; + + +/* Section - for main page content */ + +section { + width:650px; + float:right; + padding-bottom:50px; } -section #title .credits.left { - float: left; + + +/* Footer */ + +footer { + width:170px; + float:left; + position:fixed; + bottom:10px; + padding-left: 50px; } -section #title .credits.right { - float: right; + +@media print, screen and (max-width: 960px) { + + div.wrapper { + width:auto; + margin:0; + } + + header, section, footer { + float:none; + position:static; + width:auto; + } + + footer { + border-top: 1px solid #ccc; + margin:0 84px 0 50px; + padding:0; + } + + header { + padding-right:320px; + } + + section { + padding:20px 84px 20px 50px; + margin:0 0 20px; + } + + header a small { + display:inline; + } + + header ul { + position:absolute; + right:130px; + top:84px; + } } @media print, screen and (max-width: 720px) { - #title .credits { - display: block; - width: 100%; - line-height: 30px; - text-align: center; + body { + word-wrap:break-word; } - #title .credits .left { - float: none; - display: block; + + header { + padding:10px 20px 0; + margin-right: 0; } - #title .credits .right { - float: none; - display: block; + + section { + padding:10px 0 10px 20px; + margin:0 0 30px; + } + + footer { + margin: 0 0 0 30px; + } + + header ul, header p.view { + position:static; } } + @media print, screen and (max-width: 480px) { - #header { - margin-top: -20px; - } - section { - margin-top: 40px; + header ul li.download { + display:none; } - nav { - display: none; - } + footer { + margin: 0 0 0 20px; + } + + footer a{ + display:block; + } + } + +@media print { + body { + padding:0.4in; + font-size:12pt; + color:#444; + } +} \ No newline at end of file From 181a9d319c2278c3aa4e0faa3529368f173363b9 Mon Sep 17 00:00:00 2001 From: Clare Date: Mon, 2 Jun 2014 08:41:53 -0700 Subject: [PATCH 22/35] Create gh-pages branch via GitHub --- index.html | 158 +++++++++++++++++++++++++++++++++++----------------- params.json | 2 +- 2 files changed, 107 insertions(+), 53 deletions(-) diff --git a/index.html b/index.html index 1a649c13..b7d6fe3b 100644 --- a/index.html +++ b/index.html @@ -82,18 +82,31 @@

    The Open Source Data Science Curriculum

    -

    Start here.

    +

    Start here. +Intro to Data Science UW / Coursera

    • -Intro to Data Science UW / Coursera +Topics: Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.
      • +

    Data Science / Harvard Video Archive & Course

    • -Topics: Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.
      • -
    - +Topics: Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries. +

    Data Science with Open Source Tools Book

    + +
      +
    • +Topics: Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics
      • +
    • +Example Code in: R, Python, Sage, C, Gnu Scientific Library

    +A Note About Direction

    + +

    This is an introduction geared toward those with at least a minimum understanding of programming, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing). +Out of personal preference and need for focus, I geared the original curriculum toward Python tools and resources. R resources can be found here.

    + +

    Math

    OSDSM Specialization: Web Scraping & Crawling

    + + +

    OSDSM Specialization: Data Journalism

    + + + +
  • +

    iPython Data Science Notebooks

    + + +
    • +

    +Datasets are now here +

    + +

    +R resources are now here +

    Capstone Project

    @@ -284,15 +362,17 @@

  • Healthcare Twitter Analysis Coursolve & UW Data Science

    • -Further Study Resources:

    +Resources

    -Sources for Datasets:

    - -

    +

    Notation

    Paid books, courses, and resources are noted with $.

    -

    -A Note About Direction

    - -

    This is an introduction geared toward those with at least a minimum understanding of programming, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing). -Out of personal preference and need for focus, I geared the original curriculum toward Python tools and resources, so I've explicitly marked when resources use other tools to teach conceptual material (like R)

    -

    Contribute

    -

    Please Share and Contribute Your Ideas -- it's Open Source!

    - -

    Here's my transcript.

    +

    Please Contribute Your Ideas -- this is Open Source!

    -

    Please showcase your own specialization & transcript by submitting a markdown file pull request with your name! eg clare-corthell-transcript.md

    +

    Please showcase your own specialization & transcript by submitting a markdown file pull request in the /transcripts directory with your name! eg clare-corthell-2014.md

    Follow me on Twitter @clarecorthell

    diff --git a/params.json b/params.json index 3f9c8809..138481b0 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"The Open Source Data Science Masters","tagline":"Curriculum","body":"[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\n### The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*see [my transcript](http://bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://blog.bright.com/2012/11/13/how-to-hire-a-data-scientist/) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n* **Intro to Data Science** [UW / Coursera](https://www.coursera.org/course/datasci)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n### Math\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://www.math.washington.edu/~burke/crs/407/lectures/)\r\n\r\n* **Statistics**\r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://greenteapress.com/thinkstats/) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Allen Downey / Book](http://www.greenteapress.com/thinkbayes/)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](https://www.coursera.org/course/algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](https://www.coursera.org/course/datasci)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](https://www.udacity.com/course/ud617) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * SQL Tutorial [w3schools / Tutorials](http://www.w3schools.com/sql/)\r\n * SQL Tutorial [SQLZOO / Tutorials](http://sqlzoo.net/wiki/Main_Page)\r\n * Introduction to Databases [Stanford / Online Course](http://class2go.stanford.edu/db/Winter2013/)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Online Book](http://i.stanford.edu/~ullman/mmds.html)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval [Stanford / Book](http://nlp.stanford.edu/IR-book/information-retrieval-book.html)\r\n\r\n* **Machine Learning**\r\n * Machine Learning [Ng Stanford / Coursera](https://www.coursera.org/course/ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * The Elements of Statistical Learning [Stanford / Digital Book](http://www-stat.stanford.edu/~tibs/ElemStatLearn/)^\r\n * Machine Learning [Caltech / Edx](http://work.caltech.edu/lectures.html)\r\n * Neural Networks [U Toronto / Coursera](https://class.coursera.org/neuralnets-2012-001)\r\n\r\n* **Statistical Network Analysis & Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)\r\n * Probabalistic Graphical Models [Stanford / Coursera](https://www.coursera.org/course/pgm)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python (NLTK library) [Digital Book](http://nltk.org/book/), [Paper Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://blogs.ischool.berkeley.edu/i290-abdt-s12/)\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](https://www.coursera.org/course/networksonline)\r\n\r\n* **Visualization**\r\n * Envisioning Information (Information Visualization) [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * Data Visualization, CS 171 [Harvard / Lectures](http://cm.dce.harvard.edu/2014/02/24028/publicationListing.shtml)\r\n * Scott Murray's Tutorial on D3 [Blog / Tutorials](http://alignedleft.com/tutorials/)\r\n * Berkely's Viz Class [UC Berkeley / Course Docs](http://vis.berkeley.edu/courses/cs294-10-sp11/wiki/index.php/CS294-10_Visualization)\r\n * Rice University's Data Viz class [Rice University](http://had.co.nz/stat645/)\r\n\r\n* **Python** (Learning)\r\n * Learn Python the Hard Way [eBook](http://learnpythonthehardway.org/)\r\n * Python [Class / Google](http://code.google.com/edu/languages/google-python-class/)\r\n * [Think Python](http://www.greenteapress.com/thinkpython/)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-00sc-introduction-to-computer-science-and-programming-spring-2011/)\r\n\r\n* **Python** (Libraries)\r\n * Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://www.lowindata.com/2013/installing-scientific-python-on-mac-os-x/)\r\n * [Data Science in iPython Notebooks](http://nborwankar.github.io/LearnDataScience/) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * Bayesian Inference | [pymc](https://github.com/pymc-devs/pymc)\r\n * Labeled data structures objects, statistical functions, etc [pandas](https://github.com/pydata/pandas) (See: Python for Data Analysis)\r\n * Python wrapper for the Twitter API [twython](https://github.com/ryanmcgrath/twython)\r\n * Tools for Data Mining & Analysis [scikit-learn](http://scikit-learn.org/stable/)\r\n * Network Modeling & Viz [networkx](http://networkx.github.io/)\r\n * Natural Language Toolkit [NLTK](http://nltk.org/)\r\n\r\n### R resources are now [here](https://github.com/datasciencemasters/go/blob/master/r-resources.md) (Please help!)\r\n\r\n### Capstone Project\r\n* [Toy Data Ideas](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Capstone Analysis of Your Own Design; [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](https://www.coursolve.org/need/54)\r\n\r\n***\r\n### Further Study Resources:\r\n* [Coursera](http://coursera.org)\r\n* [Khan Academy](https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/term-life-insurance-and-death-probability)\r\n* [Metacademy](http://www.metacademy.org)\r\n* [Wolfram Alpha](http://www.wolframalpha.com/input/?i=torus)\r\n* [Wikipedia](http://en.wikipedia.org/wiki/List_of_cognitive_biases)\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$```](http://amzn.to/1hoxQoG)\r\n* Zipfian Academy's [List of Resources](http://blog.zipfianacademy.com/post/46864003608/a-practical-intro-to-data-science)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://www.rcasts.com/2012/12/software-engineers-guide-to-getting.html)\r\n* Data Scientist Interviews [Metamarkets](http://metamarkets.com/category/data-science/)\r\n* /r/MachineLearning [Reddit](http://www.reddit.com/r/machinelearning)\r\n\r\n### Sources for Datasets:\r\n* [NIPS Feature Selection](http://www.nipsfsc.ecs.soton.ac.uk/datasets/)\r\n* [Stanford Network Analysis Project](https://snap.stanford.edu/)\r\n* Data Science Contests [Kaggle] (https://www.kaggle.com/)\r\n* [@hmason's](https://twitter.com/hmason) curated dataset list [bit.ly](https://bitly.com/bundles/hmason/1)\r\n* Classical Datasets for Your Specific Need [UCI Machine Learning Repository Datasets](\r\nhttp://archive.ics.uci.edu/ml/datasets.html)\r\n* [Time Series Data Library](http://datamarket.com/data/list/?q=provider:tsdl)\r\n* USA Congressional Voting Records [Voteview](http://voteview.org/downloads.asp)\r\n* [Qandl](http://www.quandl.com) provides a lot of interesting data with a clean API.\r\n\r\n### Notation\r\nPaid books, courses, and resources are noted with ```$```.\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**, so I've explicitly marked when resources use other tools to teach conceptual material (like R)\r\n\r\n## Contribute\r\n\r\nPlease Share and Contribute Your Ideas -- **it's Open Source!**\r\n\r\nHere's [my transcript](https://github.com/datasciencemasters/go/wiki/%5BTranscript%5D-Clare-Corthell).\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request with your name! eg ```clare-corthell-transcript.md```\r\n\r\n[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The Open Source Data Science Masters","tagline":"Curriculum","body":"[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\n### The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*see [my transcript](http://bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://blog.bright.com/2012/11/13/how-to-hire-a-data-scientist/) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](https://www.coursera.org/course/datasci)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://cm.dce.harvard.edu/2014/01/14328/publicationListing.shtml) & [Course](http://cs109.org)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book](http://amzn.to/1m0ZXfk)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](https://github.com/datasciencemasters/go/blob/master/r-resources.md).\r\n\r\n### Math\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://www.math.washington.edu/~burke/crs/407/lectures/)\r\n\r\n* **Statistics**\r\n * Statistics One [Princeton / Coursera](https://www.coursera.org/course/stats1) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://greenteapress.com/thinkstats/) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://www.greenteapress.com/thinkbayes/) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://nbviewer.ipython.org/github/URXtech/techblog/blob/master/continuousTimeMarkovChain/markovChain.ipynb)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](https://www.coursera.org/course/algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](https://www.coursera.org/course/datasci)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](https://www.udacity.com/course/ud617) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * SQL Tutorial [w3schools / Tutorials](http://www.w3schools.com/sql/)\r\n * SQL Tutorial [SQLZOO / Tutorials](http://sqlzoo.net/wiki/Main_Page)\r\n * Introduction to Databases [Stanford / Online Course](http://class2go.stanford.edu/db/Winter2013/)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Digital](http://i.stanford.edu/~ullman/mmds.html) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://nlp.stanford.edu/IR-book/information-retrieval-book.html) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n * Machine Learning [Ng Stanford / Coursera](https://www.coursera.org/course/ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://www-stat.stanford.edu/~tibs/ElemStatLearn/)^ & [Book ```$80```](http://amzn.to/1hmyKry)\r\n * Machine Learning [Caltech / Edx](http://work.caltech.edu/lectures.html)\r\n * Neural Networks [U Toronto / Coursera](https://class.coursera.org/neuralnets-2012-001)\r\n\r\n* **Statistical Network Analysis & Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)\r\n * Probabalistic Graphical Models [Stanford / Coursera](https://www.coursera.org/course/pgm)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python (NLTK library) [Digital](http://nltk.org/book/), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://blogs.ischool.berkeley.edu/i290-abdt-s12/)\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](https://www.coursera.org/course/networksonline)\r\n\r\n* **Visualization**\r\n * Envisioning Information (Information Visualization) [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * Data Visualization, CS 171 [Harvard / Lectures](http://cm.dce.harvard.edu/2014/02/24028/publicationListing.shtml)\r\n * Scott Murray's Tutorial on D3 [Blog / Tutorials](http://alignedleft.com/tutorials/)\r\n * Berkely's Viz Class [UC Berkeley / Course Docs](http://vis.berkeley.edu/courses/cs294-10-sp11/wiki/index.php/CS294-10_Visualization)\r\n * Rice University's Data Viz class [Rice University](http://had.co.nz/stat645/)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n* **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://learnpythonthehardway.org/) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://code.google.com/edu/languages/google-python-class/)\r\n * Think Python [Digital](http://www.greenteapress.com/thinkpython/) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-00sc-introduction-to-computer-science-and-programming-spring-2011/)\r\n\r\n* **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://www.lowindata.com/2013/installing-scientific-python-on-mac-os-x/)\r\nUsing Python Scientifically [Python, NumPy, SciPy, SymPy, Matplotlib, scikit-learn](http://scipy-lectures.github.io/)\r\n\r\n * **Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](https://github.com/pydata/pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * Tools for Data Mining & Analysis [scikit-learn](http://scikit-learn.org/stable/)\r\n\r\n * **Networks**\r\n * Network Modeling & Viz [networkx](http://networkx.github.io/)\r\n\r\n * **Statistical Packages**\r\n * Bayesian Inference | [pymc](https://github.com/pymc-devs/pymc)\r\n\r\n * **Natural Language Processing & Understanding **\r\n * Natural Language Toolkit [NLTK](http://nltk.org/)\r\n\r\n * **Live Data Packages**\r\n * Python wrapper for the Twitter API [twython](https://github.com/ryanmcgrath/twython)\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://nborwankar.github.io/LearnDataScience/) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n\r\n#### Datasets are now [here](https://github.com/datasciencemasters/go/edit/master/datasets.md)\r\n\r\n#### R resources are now [here](https://github.com/datasciencemasters/go/blob/master/r-resources.md)\r\n\r\n### Capstone Project\r\n* [Toy Data Ideas](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Capstone Analysis of Your Own Design; [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](https://www.coursolve.org/need/54)\r\n\r\n***\r\n### Resources\r\n* The \"Hacker News\" of Data Science [DataTau](http://www.datatau.com)\r\n* [Coursera](http://coursera.org)\r\n* [Khan Academy](https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/term-life-insurance-and-death-probability)\r\n* [Metacademy](http://www.metacademy.org)\r\n* [Wolfram Alpha](http://www.wolframalpha.com/input/?i=torus)\r\n* [Wikipedia](http://en.wikipedia.org/wiki/List_of_cognitive_biases)\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* Zipfian Academy's [List of Resources](http://blog.zipfianacademy.com/post/46864003608/a-practical-intro-to-data-science)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://www.rcasts.com/2012/12/software-engineers-guide-to-getting.html)\r\n* Data Scientist Interviews [Metamarkets](http://metamarkets.com/category/data-science/)\r\n* /r/MachineLearning [Reddit](http://www.reddit.com/r/machinelearning)\r\n\r\n***\r\n\r\n### Notation\r\nPaid books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](https://github.com/datasciencemasters/go/wiki/%5BTranscript%5D-Clare-Corthell)\r\n\r\n[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file From 8141b813cc23e6a30c02e94daa6dcc82d755fc43 Mon Sep 17 00:00:00 2001 From: Clare Date: Tue, 17 Jun 2014 08:25:40 -0700 Subject: [PATCH 23/35] Create gh-pages branch via GitHub --- index.html | 187 +++++++++++++++++++++++++++++----------------------- params.json | 2 +- 2 files changed, 105 insertions(+), 84 deletions(-) diff --git a/index.html b/index.html index b7d6fe3b..4287135d 100644 --- a/index.html +++ b/index.html @@ -30,7 +30,7 @@

    The Open Source Data Science Masters

    -

    Follow me on Twitter @clarecorthell

    +

    Follow me on Twitter @clarecorthell

    The Open-Source Data Science Masters

    @@ -53,13 +53,13 @@

    ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.

    -

    -- McKinsey Report Highlights the Impending Data Scientist Shortage 23 July 2013

    +

    -- McKinsey Report Highlights the Impending Data Scientist Shortage 23 July 2013

    There are little to no Data Scientists with 5 years experience, because the job simply did not exist.

    -

    -- David Hardtke How To Hire A Data Scientist 13 Nov 2012

    +

    -- David Hardtke How To Hire A Data Scientist 13 Nov 2012

    An Academic Shortfall

    @@ -74,7 +74,7 @@

    And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.

    -

    -- James Kobielus, Closing the Talent Gap 17 Jan 2013

    +

    -- James Kobielus, Closing the Talent Gap 17 Jan 2013

    Ready?

    @@ -83,17 +83,17 @@

    The Open Source Data Science Curriculum

    Start here. -Intro to Data Science UW / Coursera

    +Intro to Data Science UW / Coursera

    • Topics: Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.
      • -

    Data Science / Harvard Video Archive & Course

    +

    Data Science / Harvard Video Archive & Course

    • Topics: Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.
      • -

    Data Science with Open Source Tools Book

    +

    Data Science with Open Source Tools Book

    Hosted on GitHub Pages using the Dinky theme

    diff --git a/params.json b/params.json index 138481b0..068c349a 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"The Open Source Data Science Masters","tagline":"Curriculum","body":"[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\n### The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*see [my transcript](http://bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://blog.gopivotal.com/news-2/mckinsey-report-highlights-the-impending-data-scientist-shortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://blog.bright.com/2012/11/13/how-to-hire-a-data-scientist/) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://www.ibmbigdatahub.com/blog/data-scientist-closing-talent-gap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](https://www.coursera.org/course/datasci)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://cm.dce.harvard.edu/2014/01/14328/publicationListing.shtml) & [Course](http://cs109.org)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book](http://amzn.to/1m0ZXfk)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](https://github.com/datasciencemasters/go/blob/master/r-resources.md).\r\n\r\n### Math\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://www.math.washington.edu/~burke/crs/407/lectures/)\r\n\r\n* **Statistics**\r\n * Statistics One [Princeton / Coursera](https://www.coursera.org/course/stats1) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://greenteapress.com/thinkstats/) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://www.greenteapress.com/thinkbayes/) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://nbviewer.ipython.org/github/URXtech/techblog/blob/master/continuousTimeMarkovChain/markovChain.ipynb)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](https://www.coursera.org/course/algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](https://www.coursera.org/course/datasci)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](https://www.udacity.com/course/ud617) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * SQL Tutorial [w3schools / Tutorials](http://www.w3schools.com/sql/)\r\n * SQL Tutorial [SQLZOO / Tutorials](http://sqlzoo.net/wiki/Main_Page)\r\n * Introduction to Databases [Stanford / Online Course](http://class2go.stanford.edu/db/Winter2013/)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Digital](http://i.stanford.edu/~ullman/mmds.html) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://nlp.stanford.edu/IR-book/information-retrieval-book.html) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n * Machine Learning [Ng Stanford / Coursera](https://www.coursera.org/course/ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://www-stat.stanford.edu/~tibs/ElemStatLearn/)^ & [Book ```$80```](http://amzn.to/1hmyKry)\r\n * Machine Learning [Caltech / Edx](http://work.caltech.edu/lectures.html)\r\n * Neural Networks [U Toronto / Coursera](https://class.coursera.org/neuralnets-2012-001)\r\n\r\n* **Statistical Network Analysis & Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers)\r\n * Probabalistic Graphical Models [Stanford / Coursera](https://www.coursera.org/course/pgm)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python (NLTK library) [Digital](http://nltk.org/book/), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://blogs.ischool.berkeley.edu/i290-abdt-s12/)\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](https://www.coursera.org/course/networksonline)\r\n\r\n* **Visualization**\r\n * Envisioning Information (Information Visualization) [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * Data Visualization, CS 171 [Harvard / Lectures](http://cm.dce.harvard.edu/2014/02/24028/publicationListing.shtml)\r\n * Scott Murray's Tutorial on D3 [Blog / Tutorials](http://alignedleft.com/tutorials/)\r\n * Berkely's Viz Class [UC Berkeley / Course Docs](http://vis.berkeley.edu/courses/cs294-10-sp11/wiki/index.php/CS294-10_Visualization)\r\n * Rice University's Data Viz class [Rice University](http://had.co.nz/stat645/)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n* **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://learnpythonthehardway.org/) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://code.google.com/edu/languages/google-python-class/)\r\n * Think Python [Digital](http://www.greenteapress.com/thinkpython/) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-00sc-introduction-to-computer-science-and-programming-spring-2011/)\r\n\r\n* **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://www.lowindata.com/2013/installing-scientific-python-on-mac-os-x/)\r\nUsing Python Scientifically [Python, NumPy, SciPy, SymPy, Matplotlib, scikit-learn](http://scipy-lectures.github.io/)\r\n\r\n * **Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](https://github.com/pydata/pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * Tools for Data Mining & Analysis [scikit-learn](http://scikit-learn.org/stable/)\r\n\r\n * **Networks**\r\n * Network Modeling & Viz [networkx](http://networkx.github.io/)\r\n\r\n * **Statistical Packages**\r\n * Bayesian Inference | [pymc](https://github.com/pymc-devs/pymc)\r\n\r\n * **Natural Language Processing & Understanding **\r\n * Natural Language Toolkit [NLTK](http://nltk.org/)\r\n\r\n * **Live Data Packages**\r\n * Python wrapper for the Twitter API [twython](https://github.com/ryanmcgrath/twython)\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://nborwankar.github.io/LearnDataScience/) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n\r\n#### Datasets are now [here](https://github.com/datasciencemasters/go/edit/master/datasets.md)\r\n\r\n#### R resources are now [here](https://github.com/datasciencemasters/go/blob/master/r-resources.md)\r\n\r\n### Capstone Project\r\n* [Toy Data Ideas](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)\r\n* Capstone Analysis of Your Own Design; [Quora](http://www.quora.com/Programming-Challenges-1/What-are-some-good-toy-problems-in-data-science)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](https://www.coursolve.org/need/54)\r\n\r\n***\r\n### Resources\r\n* The \"Hacker News\" of Data Science [DataTau](http://www.datatau.com)\r\n* [Coursera](http://coursera.org)\r\n* [Khan Academy](https://www.khanacademy.org/math/probability/random-variables-topic/random_variables_prob_dist/v/term-life-insurance-and-death-probability)\r\n* [Metacademy](http://www.metacademy.org)\r\n* [Wolfram Alpha](http://www.wolframalpha.com/input/?i=torus)\r\n* [Wikipedia](http://en.wikipedia.org/wiki/List_of_cognitive_biases)\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* Zipfian Academy's [List of Resources](http://blog.zipfianacademy.com/post/46864003608/a-practical-intro-to-data-science)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://www.rcasts.com/2012/12/software-engineers-guide-to-getting.html)\r\n* Data Scientist Interviews [Metamarkets](http://metamarkets.com/category/data-science/)\r\n* /r/MachineLearning [Reddit](http://www.reddit.com/r/machinelearning)\r\n\r\n***\r\n\r\n### Notation\r\nPaid books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](https://github.com/datasciencemasters/go/wiki/%5BTranscript%5D-Clare-Corthell)\r\n\r\n[Follow me on Twitter @clarecorthell](http://twitter.com/clarecorthell)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The Open Source Data Science Masters","tagline":"Curriculum","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\n### The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*see [my transcript](http://bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * SQL Tutorial [w3schools / Tutorials](http://bit.ly/tut-w3schools-sql)\r\n * SQL Tutorial [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n * Introduction to Databases [Stanford / Online Course](http://bit.ly/stanford-introdatabases)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn)^ & [Book ```$80```](http://amzn.to/1hmyKry)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Statistical Network Analysis & Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n\r\n* **Visualization**\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n * Data Visualization, CS 171 [Harvard / Lectures](http://bit.ly/harvard-dataviz)\r\n * Data Visualization, CSE512 [University of Washington / Slides](http://bit.ly/uw-dataviz)\r\n * Scott Murray's Tutorial on D3 [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Berkely's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University](http://bit.ly/riceu-viz)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n_More Libraries can be found in related [specialiaztions](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nPaid books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file From 1b42b08fa6cdc69555f59fd5afc5fcbfba05c35e Mon Sep 17 00:00:00 2001 From: Clare Date: Sun, 13 Jul 2014 23:50:26 -0700 Subject: [PATCH 24/35] Create gh-pages branch via GitHub --- index.html | 37 ++++++++++++++++++++++++++++--------- params.json | 2 +- 2 files changed, 29 insertions(+), 10 deletions(-) diff --git a/index.html b/index.html index 4287135d..e181193f 100644 --- a/index.html +++ b/index.html @@ -17,7 +17,7 @@

    The Open Source Data Science Masters

    -

    Curriculum

    +

    Curriculum for Data Science

    + +
  • +

    Network & Graph Analysis

    + +
  • @@ -244,7 +256,7 @@

  • Big Data Analysis with Twitter UC Berkeley / Lectures
    • -
  • Social and Economic Networks: Models and Analysis / Stanford / Coursera +
  • Exploratory Data Analysis Tukey / Book $81
    • @@ -361,6 +373,7 @@

    @@ -372,6 +385,12 @@

    +Data Science as a Profession

    + +

    Capstone Project

      diff --git a/params.json b/params.json index 068c349a..2827445d 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"The Open Source Data Science Masters","tagline":"Curriculum","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\n### The Internet is Your Oyster\r\n\r\nI didn't want to wait. I wanted to work on things I care about **now**. Why sleep through grad school lectures tomorrow when you can hack on interesting questions today?\r\n\r\n*see [my transcript](http://bit.ly/corthelldata)*\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * SQL Tutorial [w3schools / Tutorials](http://bit.ly/tut-w3schools-sql)\r\n * SQL Tutorial [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n * Introduction to Databases [Stanford / Online Course](http://bit.ly/stanford-introdatabases)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn)^ & [Book ```$80```](http://amzn.to/1hmyKry)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Statistical Network Analysis & Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n\r\n* **Visualization**\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n * Data Visualization, CS 171 [Harvard / Lectures](http://bit.ly/harvard-dataviz)\r\n * Data Visualization, CSE512 [University of Washington / Slides](http://bit.ly/uw-dataviz)\r\n * Scott Murray's Tutorial on D3 [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Berkely's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University](http://bit.ly/riceu-viz)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n_More Libraries can be found in related [specialiaztions](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nPaid books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The Open Source Data Science Masters","tagline":"Curriculum for Data Science","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\nThe open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.\r\n\r\n### The Internet is Your Oyster\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n\r\n[★ What are some good resources for learning about numerical analysis? / Quora ]\r\n(http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)\r\n\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * SQL Tutorial [w3schools / Tutorials](http://bit.ly/tut-w3schools-sql)\r\n * SQL Tutorial [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n * Introduction to Databases [Stanford / Online Course](http://bit.ly/stanford-introdatabases)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn)^ & [Book ```$80```](http://amzn.to/1hmyKry)\r\n * Machine Learning for Hackers - Python port [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)\r\n\r\n* **Statistical Network Analysis & Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Network & Graph Analysis**\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)\r\n\r\n* **Visualization**\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n * Data Visualization, CS 171 [Harvard / Lectures](http://bit.ly/harvard-dataviz)\r\n * Data Visualization, CSE512 [University of Washington / Slides](http://bit.ly/uw-dataviz)\r\n * Scott Murray's Tutorial on D3 [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Berkely's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University](http://bit.ly/riceu-viz)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n_More Libraries can be found in related [specialiaztions](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Data Science as a Profession\r\n\r\n * Doing Data Science: Straight Talk from the Frontline [O'Reilly / Book ```$25```](http://amzn.to/1vAIscK)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nPaid books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file From f15ef1413ec0bebce00cad237dffd3808292d295 Mon Sep 17 00:00:00 2001 From: Clare Date: Mon, 14 Jul 2014 16:59:32 -0700 Subject: [PATCH 25/35] Create gh-pages branch via GitHub --- index.html | 8 ++++---- params.json | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/index.html b/index.html index e181193f..0d0d6691 100644 --- a/index.html +++ b/index.html @@ -91,7 +91,7 @@

      • Topics: Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.
        • -

      Data Science with Open Source Tools Book

      +

    Data Science with Open Source Tools Book $27

    • @@ -177,11 +177,11 @@

      Databases

      • diff --git a/params.json b/params.json index 2827445d..5ea681e7 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"The Open Source Data Science Masters","tagline":"Curriculum for Data Science","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\nThe open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.\r\n\r\n### The Internet is Your Oyster\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n\r\n[★ What are some good resources for learning about numerical analysis? / Quora ]\r\n(http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)\r\n\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * SQL Tutorial [w3schools / Tutorials](http://bit.ly/tut-w3schools-sql)\r\n * SQL Tutorial [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n * Introduction to Databases [Stanford / Online Course](http://bit.ly/stanford-introdatabases)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn)^ & [Book ```$80```](http://amzn.to/1hmyKry)\r\n * Machine Learning for Hackers - Python port [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)\r\n\r\n* **Statistical Network Analysis & Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Network & Graph Analysis**\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)\r\n\r\n* **Visualization**\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n * Data Visualization, CS 171 [Harvard / Lectures](http://bit.ly/harvard-dataviz)\r\n * Data Visualization, CSE512 [University of Washington / Slides](http://bit.ly/uw-dataviz)\r\n * Scott Murray's Tutorial on D3 [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Berkely's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University](http://bit.ly/riceu-viz)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n_More Libraries can be found in related [specialiaztions](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Data Science as a Profession\r\n\r\n * Doing Data Science: Straight Talk from the Frontline [O'Reilly / Book ```$25```](http://amzn.to/1vAIscK)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nPaid books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The Open Source Data Science Masters","tagline":"Curriculum for Data Science","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\nThe open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.\r\n\r\n### The Internet is Your Oyster\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book ```$27```](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n\r\n[★ What are some good resources for learning about numerical analysis? / Quora ]\r\n(http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)\r\n\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Online Course](http://bit.ly/stanford-introdatabases)\r\n * SQL School [Mode Analytics / Tutorials](http://sqlschool.modeanalytics.com/)\r\n * SQL Tutorials [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn)^ & [Book ```$80```](http://amzn.to/1hmyKry)\r\n * Machine Learning for Hackers - Python port [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)\r\n\r\n* **Statistical Network Analysis & Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Network & Graph Analysis**\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)\r\n\r\n* **Visualization**\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n * Data Visualization, CS 171 [Harvard / Lectures](http://bit.ly/harvard-dataviz)\r\n * Data Visualization, CSE512 [University of Washington / Slides](http://bit.ly/uw-dataviz)\r\n * Scott Murray's Tutorial on D3 [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Berkely's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University](http://bit.ly/riceu-viz)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n_More Libraries can be found in related [specialiaztions](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Data Science as a Profession\r\n\r\n * Doing Data Science: Straight Talk from the Frontline [O'Reilly / Book ```$25```](http://amzn.to/1vAIscK)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nPaid books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n","google":"UA-43720588-1","note":"Don't delete this file! 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      -
      -

      The Open Source Data Science Masters

      -

      Curriculum for Data Science

      - + +
      +
      + View on GitHub -

      This project is maintained by datasciencemasters

      +

      The Open Source Data Science Masters

      +

      Curriculum for Data Science

      +
      + Download this project as a .zip file + Download this project as a tar.gz file +
      +
      +
      -
      -
      + +
      +

      Follow me on Twitter @clarecorthell

      @@ -189,7 +190,7 @@

      Data Mining

      +

    +Data Design

    + +

    Notation

    -

    Paid books, courses, and resources are noted with $.

    +

    Non-Open-Source books, courses, and resources are noted with $.

    Contribute

    @@ -435,12 +460,17 @@

    Follow me on Twitter @clarecorthell

    -

    + + + - - @@ -451,5 +481,6 @@

    } catch(err) {} + diff --git a/javascripts/main.js b/javascripts/main.js index c57e54c3..d8135d37 100644 --- a/javascripts/main.js +++ b/javascripts/main.js @@ -1,53 +1 @@ -var sectionHeight = function() { - var total = $(window).height(), - $section = $('section').css('height','auto'); - - if ($section.outerHeight(true) < total) { - var margin = $section.outerHeight(true) - $section.height(); - $section.height(total - margin - 20); - } else { - $section.css('height','auto'); - } -} - -$(window).resize(sectionHeight); - -$(document).ready(function(){ - $("section h1, section h2").each(function(){ - $("nav ul").append("
  • " + $(this).text() + "
    • "); - $(this).attr("id",$(this).text().toLowerCase().replace(/ /g, '-').replace(/[^\w-]+/g,'')); - $("nav ul li:first-child a").parent().addClass("active"); - }); - - $("nav ul li").on("click", "a", function(event) { - var position = $($(this).attr("href")).offset().top - 190; - $("html, body").animate({scrollTop: position}, 400); - $("nav ul li a").parent().removeClass("active"); - $(this).parent().addClass("active"); - event.preventDefault(); - }); - - sectionHeight(); - - $('img').load(sectionHeight); -}); - -fixScale = function(doc) { - - var addEvent = 'addEventListener', - type = 'gesturestart', - qsa = 'querySelectorAll', - scales = [1, 1], - meta = qsa in doc ? doc[qsa]('meta[name=viewport]') : []; - - function fix() { - meta.content = 'width=device-width,minimum-scale=' + scales[0] + ',maximum-scale=' + scales[1]; - doc.removeEventListener(type, fix, true); - } - - if ((meta = meta[meta.length - 1]) && addEvent in doc) { - fix(); - scales = [.25, 1.6]; - doc[addEvent](type, fix, true); - } -}; \ No newline at end of file +console.log('This would be the main JS file.'); diff --git a/params.json b/params.json index 5ea681e7..a6abbb3b 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"The Open Source Data Science Masters","tagline":"Curriculum for Data Science","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\nThe open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.\r\n\r\n### The Internet is Your Oyster\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book ```$27```](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n\r\n[★ What are some good resources for learning about numerical analysis? / Quora ]\r\n(http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)\r\n\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Online Course](http://bit.ly/stanford-introdatabases)\r\n * SQL School [Mode Analytics / Tutorials](http://sqlschool.modeanalytics.com/)\r\n * SQL Tutorials [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets [Stanford / Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn)^ & [Book ```$80```](http://amzn.to/1hmyKry)\r\n * Machine Learning for Hackers - Python port [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)\r\n\r\n* **Statistical Network Analysis & Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Network & Graph Analysis**\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)\r\n\r\n* **Visualization**\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n * Data Visualization, CS 171 [Harvard / Lectures](http://bit.ly/harvard-dataviz)\r\n * Data Visualization, CSE512 [University of Washington / Slides](http://bit.ly/uw-dataviz)\r\n * Scott Murray's Tutorial on D3 [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Berkely's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University](http://bit.ly/riceu-viz)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n_More Libraries can be found in related [specialiaztions](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Data Science as a Profession\r\n\r\n * Doing Data Science: Straight Talk from the Frontline [O'Reilly / Book ```$25```](http://amzn.to/1vAIscK)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nPaid books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The Open Source Data Science Masters","tagline":"Curriculum for Data Science","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\nThe open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.\r\n\r\n### The Internet is Your Oyster\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book ```$27```](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n\r\n[★ What are some good resources for learning about numerical analysis? / Quora ]\r\n(http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)\r\n\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Online Course](http://bit.ly/stanford-introdatabases)\r\n * SQL School [Mode Analytics / Tutorials](http://sqlschool.modeanalytics.com/)\r\n * SQL Tutorials [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets / Stanford [Coursera](https://www.coursera.org/course/mmds) & [Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n\r\n _Foundational & Theoretical_\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn) & [Book ```$80```](http://amzn.to/1hmyKry)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n\r\n _Practical_\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * Machine Learning for Hackers [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)\r\n * Intro to scikit-learn, SciPy2013 [youtube tutorials](http://bit.ly/scikit-video-tuts)\r\n\r\n* **Statistical Network Analysis & Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Network & Graph Analysis**\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)\r\n\r\n### Data Design\r\n\r\n* **Visualization**\r\n\r\n _Foundational Information Design Books_\r\n\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n\r\n _Theoretical Courses / Design & Visualization_\r\n\r\n * Data Visualization [University of Washington / Slides & Resources](http://bit.ly/uw-dataviz)\r\n * Berkeley's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University / Slides](http://bit.ly/riceu-viz)\r\n\r\n _Practical Visualization Resources_\r\n\r\n * D3 Library / Scott Murray [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Interactive Data Visualization for the Web / Scott Murray [Online Book](bit.ly/interactive-data-viz-web) & [Book `$26`](http://amzn.to/1oK1xCN)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n_More Libraries can be found in related [specialiaztions](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [matplotlib](http://www.ast.uct.ac.za/~sarblyth/pythonGuide/PythonPlottingBeginnersGuide.pdf) - well-integrated with analysis and data manipulation packages like numpy and pandas\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Data Science as a Profession\r\n\r\n * Doing Data Science: Straight Talk from the Frontline [O'Reilly / Book ```$25```](http://amzn.to/1vAIscK)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nNon-Open-Source books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n","google":"UA-43720588-1","note":"Don't delete this file! 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+ } + + .inner { + min-width: 320px; + max-width: 480px; + } + + #project_title { + font-size: 32px; + } + + h1 { + font-size: 28px; + } + + h2 { + font-size: 24px; + } + + h3 { + font-size: 21px; + } + + h4 { + font-size: 18px; + } + + h5 { + font-size: 14px; + } + + h6 { + font-size: 12px; + } + + code, pre { + min-width: 320px; + max-width: 480px; + font-size: 11px; + } + +} From 8ebfa3239d0e70c05c6fa112bffdf1fe11002187 Mon Sep 17 00:00:00 2001 From: Clare Date: Sat, 11 Oct 2014 22:15:34 -0700 Subject: [PATCH 27/35] Create gh-pages branch via GitHub --- index.html | 67 ++++++++++++++++++++++++++++++++++++++++++----------- params.json | 2 +- 2 files changed, 54 insertions(+), 15 deletions(-) diff --git a/index.html b/index.html index 04f78849..d5fee57c 100644 --- a/index.html +++ b/index.html @@ -78,7 +78,9 @@

    Ready?

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    The Open Source Data Science Curriculum

    Start here. @@ -87,19 +89,25 @@

    • Topics: Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.
      • -

    Data Science / Harvard Video Archive & Course

    + + +

    Data Science / Harvard Video Archive & Course

    • Topics: Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.
      • -

    Data Science with Open Source Tools Book $27

    + + +

    Data Science with Open Source Tools Book $27

    • Topics: Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics
    • Example Code in: R, Python, Sage, C, Gnu Scientific Library
      • -

    + + +

    A Note About Direction

    This is an introduction geared toward those with at least a minimum understanding of programming, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing). @@ -150,7 +158,9 @@

    -

    + + +

    Computing

      @@ -198,7 +208,9 @@

    -

    OSDSM Specialization: Web Scraping & Crawling

    + + +

    OSDSM Specialization: Web Scraping & Crawling

    +

    Practical

    @@ -253,6 +268,8 @@

    Natural Language Processing

    @@ -269,7 +286,9 @@

    -

    + + +

    Data Design

      @@ -284,6 +303,7 @@

    • The Visual Display of Quantitative Information Tufte / Book $27

    +

    Theoretical Courses / Design & Visualization

    +

    Practical Visualization Resources

      @@ -303,7 +324,9 @@

    -

    OSDSM Specialization: Data Journalism

    + + +

    OSDSM Specialization: Data Journalism

    @@ -318,7 +341,9 @@

  • Introduction to Computer Science and Programming MIT OpenCourseWare / Lectures
    • -

    + + +

    Python (Libraries)

    @@ -391,7 +416,9 @@

    Orange - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining -
      +
    + + -

    + + +

    Datasets are now here

    @@ -415,14 +444,20 @@

    + + +

    Capstone Project

    + + +
    + +

    Resources

      @@ -446,7 +481,11 @@

    • /r/MachineLearning Reddit
      • -

    + + +
    + +

    Notation

    Non-Open-Source books, courses, and resources are noted with $.

    diff --git a/params.json b/params.json index a6abbb3b..b4ed0ad4 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"The Open Source Data Science Masters","tagline":"Curriculum for Data Science","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\nThe open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.\r\n\r\n### The Internet is Your Oyster\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book ```$27```](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n\r\n[★ What are some good resources for learning about numerical analysis? / Quora ]\r\n(http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)\r\n\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Online Course](http://bit.ly/stanford-introdatabases)\r\n * SQL School [Mode Analytics / Tutorials](http://sqlschool.modeanalytics.com/)\r\n * SQL Tutorials [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets / Stanford [Coursera](https://www.coursera.org/course/mmds) & [Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n\r\n _Foundational & Theoretical_\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn) & [Book ```$80```](http://amzn.to/1hmyKry)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n\r\n _Practical_\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * Machine Learning for Hackers [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)\r\n * Intro to scikit-learn, SciPy2013 [youtube tutorials](http://bit.ly/scikit-video-tuts)\r\n\r\n* **Statistical Network Analysis & Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Network & Graph Analysis**\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)\r\n\r\n* **Natural Language Processing**\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)\r\n\r\n### Data Design\r\n\r\n* **Visualization**\r\n\r\n _Foundational Information Design Books_\r\n\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n\r\n _Theoretical Courses / Design & Visualization_\r\n\r\n * Data Visualization [University of Washington / Slides & Resources](http://bit.ly/uw-dataviz)\r\n * Berkeley's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University / Slides](http://bit.ly/riceu-viz)\r\n\r\n _Practical Visualization Resources_\r\n\r\n * D3 Library / Scott Murray [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Interactive Data Visualization for the Web / Scott Murray [Online Book](bit.ly/interactive-data-viz-web) & [Book `$26`](http://amzn.to/1oK1xCN)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n_More Libraries can be found in related [specialiaztions](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [matplotlib](http://www.ast.uct.ac.za/~sarblyth/pythonGuide/PythonPlottingBeginnersGuide.pdf) - well-integrated with analysis and data manipulation packages like numpy and pandas\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Data Science as a Profession\r\n\r\n * Doing Data Science: Straight Talk from the Frontline [O'Reilly / Book ```$25```](http://amzn.to/1vAIscK)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nNon-Open-Source books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The Open Source Data Science Masters","tagline":"Curriculum for Data Science","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\nThe open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.\r\n\r\n### The Internet is Your Oyster\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book ```$27```](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n\r\n[★ What are some good resources for learning about numerical analysis? / Quora ]\r\n(http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)\r\n\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Online Course](http://bit.ly/stanford-introdatabases)\r\n * SQL School [Mode Analytics / Tutorials](http://sqlschool.modeanalytics.com/)\r\n * SQL Tutorials [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets / Stanford [Coursera](https://www.coursera.org/course/mmds) & [Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n\r\n _Foundational & Theoretical_\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn) & [Book ```$80```](http://amzn.to/1hmyKry)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n\r\n _Practical_\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * Machine Learning for Hackers [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)\r\n * Intro to scikit-learn, SciPy2013 [youtube tutorials](http://bit.ly/scikit-video-tuts)\r\n\r\n* **Statistical Network Analysis & Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n * Neural Networks (Deep Learning) [Andrej Karpathy / Python Walkthrough](http://karpathy.github.io/neuralnets/)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Network & Graph Analysis**\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)\r\n\r\n* **Natural Language Processing**\r\n * From Languages to Information / Stanford CS147 [Materials](http://web.stanford.edu/class/cs124/)\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)\r\n\r\n### Data Design\r\n\r\n* **Visualization**\r\n\r\n _Foundational Information Design Books_\r\n\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n\r\n _Theoretical Courses / Design & Visualization_\r\n\r\n * Data Visualization [University of Washington / Slides & Resources](http://bit.ly/uw-dataviz)\r\n * Berkeley's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University / Slides](http://bit.ly/riceu-viz)\r\n\r\n _Practical Visualization Resources_\r\n\r\n * D3 Library / Scott Murray [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Interactive Data Visualization for the Web / Scott Murray [Online Book](bit.ly/interactive-data-viz-web) & [Book `$26`](http://amzn.to/1oK1xCN)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n_More Libraries can be found in related [specialiaztions](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [matplotlib](http://www.ast.uct.ac.za/~sarblyth/pythonGuide/PythonPlottingBeginnersGuide.pdf) - well-integrated with analysis and data manipulation packages like numpy and pandas\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Data Science as a Profession\r\n\r\n * Doing Data Science: Straight Talk from the Frontline [O'Reilly / Book ```$25```](http://amzn.to/1vAIscK)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nNon-Open-Source books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file From 6efd61e28036a634c5b7f15ea3ca02d3b0706a26 Mon Sep 17 00:00:00 2001 From: Clare Date: Sat, 11 Oct 2014 22:21:31 -0700 Subject: [PATCH 28/35] Create gh-pages branch via GitHub --- index.html | 12 +++++++++--- params.json | 2 +- 2 files changed, 10 insertions(+), 4 deletions(-) diff --git a/index.html b/index.html index d5fee57c..c99bccf4 100644 --- a/index.html +++ b/index.html @@ -241,21 +241,27 @@

  • -

    Statistical Network Analysis & Modeling

    +

    Probabilistic Modeling

    +
    • +
  • +

    Deep Learning (Neural Networks)

    + +
  • -

    Network & Graph Analysis

    +

    Social Network & Graph Analysis

    • Social and Economic Networks: Models and Analysis / Stanford / Coursera diff --git a/params.json b/params.json index b4ed0ad4..6fe90afd 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"The Open Source Data Science Masters","tagline":"Curriculum for Data Science","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\nThe open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.\r\n\r\n### The Internet is Your Oyster\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book ```$27```](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n\r\n[★ What are some good resources for learning about numerical analysis? / Quora ]\r\n(http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)\r\n\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Online Course](http://bit.ly/stanford-introdatabases)\r\n * SQL School [Mode Analytics / Tutorials](http://sqlschool.modeanalytics.com/)\r\n * SQL Tutorials [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets / Stanford [Coursera](https://www.coursera.org/course/mmds) & [Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n\r\n _Foundational & Theoretical_\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn) & [Book ```$80```](http://amzn.to/1hmyKry)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n\r\n _Practical_\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * Machine Learning for Hackers [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)\r\n * Intro to scikit-learn, SciPy2013 [youtube tutorials](http://bit.ly/scikit-video-tuts)\r\n\r\n* **Statistical Network Analysis & Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n * Neural Networks (Deep Learning) [Andrej Karpathy / Python Walkthrough](http://karpathy.github.io/neuralnets/)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Network & Graph Analysis**\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)\r\n\r\n* **Natural Language Processing**\r\n * From Languages to Information / Stanford CS147 [Materials](http://web.stanford.edu/class/cs124/)\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)\r\n\r\n### Data Design\r\n\r\n* **Visualization**\r\n\r\n _Foundational Information Design Books_\r\n\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n\r\n _Theoretical Courses / Design & Visualization_\r\n\r\n * Data Visualization [University of Washington / Slides & Resources](http://bit.ly/uw-dataviz)\r\n * Berkeley's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University / Slides](http://bit.ly/riceu-viz)\r\n\r\n _Practical Visualization Resources_\r\n\r\n * D3 Library / Scott Murray [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Interactive Data Visualization for the Web / Scott Murray [Online Book](bit.ly/interactive-data-viz-web) & [Book `$26`](http://amzn.to/1oK1xCN)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n_More Libraries can be found in related [specialiaztions](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [matplotlib](http://www.ast.uct.ac.za/~sarblyth/pythonGuide/PythonPlottingBeginnersGuide.pdf) - well-integrated with analysis and data manipulation packages like numpy and pandas\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Data Science as a Profession\r\n\r\n * Doing Data Science: Straight Talk from the Frontline [O'Reilly / Book ```$25```](http://amzn.to/1vAIscK)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nNon-Open-Source books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The Open Source Data Science Masters","tagline":"Curriculum for Data Science","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\nThe open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.\r\n\r\n### The Internet is Your Oyster\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book ```$27```](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n\r\n[★ What are some good resources for learning about numerical analysis? / Quora ]\r\n(http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)\r\n\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Online Course](http://bit.ly/stanford-introdatabases)\r\n * SQL School [Mode Analytics / Tutorials](http://sqlschool.modeanalytics.com/)\r\n * SQL Tutorials [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets / Stanford [Coursera](https://www.coursera.org/course/mmds) & [Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n\r\n _Foundational & Theoretical_\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn) & [Book ```$80```](http://amzn.to/1hmyKry)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n\r\n _Practical_\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * Machine Learning for Hackers [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)\r\n * Intro to scikit-learn, SciPy2013 [youtube tutorials](http://bit.ly/scikit-video-tuts)\r\n\r\n* **Probabilistic Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n\r\n* **Deep Learning (Neural Networks)**\r\n * Neural Networks [Andrej Karpathy / Python Walkthrough](http://karpathy.github.io/neuralnets/)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Social Network & Graph Analysis**\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)\r\n\r\n* **Natural Language Processing**\r\n * From Languages to Information / Stanford CS147 [Materials](http://web.stanford.edu/class/cs124/)\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)\r\n\r\n### Data Design\r\n\r\n* **Visualization**\r\n\r\n _Foundational Information Design Books_\r\n\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n\r\n _Theoretical Courses / Design & Visualization_\r\n\r\n * Data Visualization [University of Washington / Slides & Resources](http://bit.ly/uw-dataviz)\r\n * Berkeley's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University / Slides](http://bit.ly/riceu-viz)\r\n\r\n _Practical Visualization Resources_\r\n\r\n * D3 Library / Scott Murray [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Interactive Data Visualization for the Web / Scott Murray [Online Book](bit.ly/interactive-data-viz-web) & [Book `$26`](http://amzn.to/1oK1xCN)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n_More Libraries can be found in related [specialiaztions](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [matplotlib](http://www.ast.uct.ac.za/~sarblyth/pythonGuide/PythonPlottingBeginnersGuide.pdf) - well-integrated with analysis and data manipulation packages like numpy and pandas\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Data Science as a Profession\r\n\r\n * Doing Data Science: Straight Talk from the Frontline [O'Reilly / Book ```$25```](http://amzn.to/1vAIscK)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nNon-Open-Source books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file From b0a7af179db98fe0a8d1d9faf200ee141dec240c Mon Sep 17 00:00:00 2001 From: Clare Date: Sat, 11 Oct 2014 23:38:35 -0700 Subject: [PATCH 29/35] Create gh-pages branch via GitHub --- index.html | 2 ++ params.json | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/index.html b/index.html index c99bccf4..4b6d8e17 100644 --- a/index.html +++ b/index.html @@ -33,6 +33,8 @@

      Curriculum for Data Science

      Follow me on Twitter @clarecorthell

      +

      Build cutting-edge Machine Learning applications with me at Mattermark --> email me!

      +

      The Open-Source Data Science Masters

      diff --git a/params.json b/params.json index 6fe90afd..b4035c1f 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"The Open Source Data Science Masters","tagline":"Curriculum for Data Science","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\nThe open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.\r\n\r\n### The Internet is Your Oyster\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book ```$27```](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n\r\n[★ What are some good resources for learning about numerical analysis? / Quora ]\r\n(http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)\r\n\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Online Course](http://bit.ly/stanford-introdatabases)\r\n * SQL School [Mode Analytics / Tutorials](http://sqlschool.modeanalytics.com/)\r\n * SQL Tutorials [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets / Stanford [Coursera](https://www.coursera.org/course/mmds) & [Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n\r\n _Foundational & Theoretical_\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn) & [Book ```$80```](http://amzn.to/1hmyKry)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n\r\n _Practical_\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * Machine Learning for Hackers [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)\r\n * Intro to scikit-learn, SciPy2013 [youtube tutorials](http://bit.ly/scikit-video-tuts)\r\n\r\n* **Probabilistic Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n\r\n* **Deep Learning (Neural Networks)**\r\n * Neural Networks [Andrej Karpathy / Python Walkthrough](http://karpathy.github.io/neuralnets/)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Social Network & Graph Analysis**\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)\r\n\r\n* **Natural Language Processing**\r\n * From Languages to Information / Stanford CS147 [Materials](http://web.stanford.edu/class/cs124/)\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)\r\n\r\n### Data Design\r\n\r\n* **Visualization**\r\n\r\n _Foundational Information Design Books_\r\n\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n\r\n _Theoretical Courses / Design & Visualization_\r\n\r\n * Data Visualization [University of Washington / Slides & Resources](http://bit.ly/uw-dataviz)\r\n * Berkeley's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University / Slides](http://bit.ly/riceu-viz)\r\n\r\n _Practical Visualization Resources_\r\n\r\n * D3 Library / Scott Murray [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Interactive Data Visualization for the Web / Scott Murray [Online Book](bit.ly/interactive-data-viz-web) & [Book `$26`](http://amzn.to/1oK1xCN)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n_More Libraries can be found in related [specialiaztions](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [matplotlib](http://www.ast.uct.ac.za/~sarblyth/pythonGuide/PythonPlottingBeginnersGuide.pdf) - well-integrated with analysis and data manipulation packages like numpy and pandas\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Data Science as a Profession\r\n\r\n * Doing Data Science: Straight Talk from the Frontline [O'Reilly / Book ```$25```](http://amzn.to/1vAIscK)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nNon-Open-Source books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The Open Source Data Science Masters","tagline":"Curriculum for Data Science","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\nBuild cutting-edge Machine Learning applications with me at [Mattermark](http://www.mattermark.com/app/jobs) --> [email me!](mailto:clare+osdsm@mattermark.com)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\nThe open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.\r\n\r\n### The Internet is Your Oyster\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book ```$27```](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n\r\n[★ What are some good resources for learning about numerical analysis? / Quora ]\r\n(http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)\r\n\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Online Course](http://bit.ly/stanford-introdatabases)\r\n * SQL School [Mode Analytics / Tutorials](http://sqlschool.modeanalytics.com/)\r\n * SQL Tutorials [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets / Stanford [Coursera](https://www.coursera.org/course/mmds) & [Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n\r\n _Foundational & Theoretical_\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn) & [Book ```$80```](http://amzn.to/1hmyKry)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n\r\n _Practical_\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * Machine Learning for Hackers [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)\r\n * Intro to scikit-learn, SciPy2013 [youtube tutorials](http://bit.ly/scikit-video-tuts)\r\n\r\n* **Probabilistic Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n\r\n* **Deep Learning (Neural Networks)**\r\n * Neural Networks [Andrej Karpathy / Python Walkthrough](http://karpathy.github.io/neuralnets/)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Social Network & Graph Analysis**\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)\r\n\r\n* **Natural Language Processing**\r\n * From Languages to Information / Stanford CS147 [Materials](http://web.stanford.edu/class/cs124/)\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)\r\n\r\n### Data Design\r\n\r\n* **Visualization**\r\n\r\n _Foundational Information Design Books_\r\n\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n\r\n _Theoretical Courses / Design & Visualization_\r\n\r\n * Data Visualization [University of Washington / Slides & Resources](http://bit.ly/uw-dataviz)\r\n * Berkeley's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University / Slides](http://bit.ly/riceu-viz)\r\n\r\n _Practical Visualization Resources_\r\n\r\n * D3 Library / Scott Murray [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Interactive Data Visualization for the Web / Scott Murray [Online Book](bit.ly/interactive-data-viz-web) & [Book `$26`](http://amzn.to/1oK1xCN)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n_More Libraries can be found in related [specialiaztions](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [matplotlib](http://www.ast.uct.ac.za/~sarblyth/pythonGuide/PythonPlottingBeginnersGuide.pdf) - well-integrated with analysis and data manipulation packages like numpy and pandas\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Data Science as a Profession\r\n\r\n * Doing Data Science: Straight Talk from the Frontline [O'Reilly / Book ```$25```](http://amzn.to/1vAIscK)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nNon-Open-Source books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file From 9d1ef333a2975587ab5e7d317f50b1b3bb116589 Mon Sep 17 00:00:00 2001 From: Clare Date: Fri, 17 Oct 2014 14:54:42 -0700 Subject: [PATCH 30/35] Create gh-pages branch via GitHub --- index.html | 4 ++-- params.json | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/index.html b/index.html index 4b6d8e17..60f1f2d2 100644 --- a/index.html +++ b/index.html @@ -33,7 +33,7 @@

      Curriculum for Data Science

      Follow me on Twitter @clarecorthell

      -

      Build cutting-edge Machine Learning applications with me at Mattermark --> email me!

      +

      Build cutting-edge Machine Learning applications with me at Mattermark --> email me!

      The Open-Source Data Science Masters

      @@ -225,7 +225,7 @@

    • A Course in Machine Learning UMD / Digital Book
      • -
    • The Elements of Statistical Learning / Stanford Digital & Book $80 +
    • The Elements of Statistical Learning / Stanford Digital & Book $80 & Study Group
    • Machine Learning Caltech / Edx
      • diff --git a/params.json b/params.json index b4035c1f..97e14a98 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"The Open Source Data Science Masters","tagline":"Curriculum for Data Science","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\nBuild cutting-edge Machine Learning applications with me at [Mattermark](http://www.mattermark.com/app/jobs) --> [email me!](mailto:clare+osdsm@mattermark.com)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\nThe open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.\r\n\r\n### The Internet is Your Oyster\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book ```$27```](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n\r\n[★ What are some good resources for learning about numerical analysis? / Quora ]\r\n(http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)\r\n\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Online Course](http://bit.ly/stanford-introdatabases)\r\n * SQL School [Mode Analytics / Tutorials](http://sqlschool.modeanalytics.com/)\r\n * SQL Tutorials [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets / Stanford [Coursera](https://www.coursera.org/course/mmds) & [Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n\r\n _Foundational & Theoretical_\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn) & [Book ```$80```](http://amzn.to/1hmyKry)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n\r\n _Practical_\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * Machine Learning for Hackers [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)\r\n * Intro to scikit-learn, SciPy2013 [youtube tutorials](http://bit.ly/scikit-video-tuts)\r\n\r\n* **Probabilistic Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n\r\n* **Deep Learning (Neural Networks)**\r\n * Neural Networks [Andrej Karpathy / Python Walkthrough](http://karpathy.github.io/neuralnets/)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Social Network & Graph Analysis**\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)\r\n\r\n* **Natural Language Processing**\r\n * From Languages to Information / Stanford CS147 [Materials](http://web.stanford.edu/class/cs124/)\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)\r\n\r\n### Data Design\r\n\r\n* **Visualization**\r\n\r\n _Foundational Information Design Books_\r\n\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n\r\n _Theoretical Courses / Design & Visualization_\r\n\r\n * Data Visualization [University of Washington / Slides & Resources](http://bit.ly/uw-dataviz)\r\n * Berkeley's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University / Slides](http://bit.ly/riceu-viz)\r\n\r\n _Practical Visualization Resources_\r\n\r\n * D3 Library / Scott Murray [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Interactive Data Visualization for the Web / Scott Murray [Online Book](bit.ly/interactive-data-viz-web) & [Book `$26`](http://amzn.to/1oK1xCN)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n_More Libraries can be found in related [specialiaztions](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [matplotlib](http://www.ast.uct.ac.za/~sarblyth/pythonGuide/PythonPlottingBeginnersGuide.pdf) - well-integrated with analysis and data manipulation packages like numpy and pandas\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Data Science as a Profession\r\n\r\n * Doing Data Science: Straight Talk from the Frontline [O'Reilly / Book ```$25```](http://amzn.to/1vAIscK)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nNon-Open-Source books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The Open Source Data Science Masters","tagline":"Curriculum for Data Science","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\nBuild cutting-edge Machine Learning applications with me at [Mattermark](http://www.mattermark.com/app/jobs) --> [email me!](http://www.google.com/recaptcha/mailhide/d?k=01KgESkAshOFOWRMyGkcdCEQ==&c=Cmjc2-OpRImyczz0qZ2fupPU5hPh_xmcRLiawYbBE8s=)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\nThe open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.\r\n\r\n### The Internet is Your Oyster\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book ```$27```](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n\r\n[★ What are some good resources for learning about numerical analysis? / Quora ]\r\n(http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)\r\n\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Online Course](http://bit.ly/stanford-introdatabases)\r\n * SQL School [Mode Analytics / Tutorials](http://sqlschool.modeanalytics.com/)\r\n * SQL Tutorials [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets / Stanford [Coursera](https://www.coursera.org/course/mmds) & [Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n\r\n _Foundational & Theoretical_\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn) & [Book ```$80```](http://amzn.to/1hmyKry) & [Study Group](http://www.reddit.com/r/eosl)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n\r\n _Practical_\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * Machine Learning for Hackers [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)\r\n * Intro to scikit-learn, SciPy2013 [youtube tutorials](http://bit.ly/scikit-video-tuts)\r\n\r\n* **Probabilistic Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n\r\n* **Deep Learning (Neural Networks)**\r\n * Neural Networks [Andrej Karpathy / Python Walkthrough](http://karpathy.github.io/neuralnets/)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Social Network & Graph Analysis**\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)\r\n\r\n* **Natural Language Processing**\r\n * From Languages to Information / Stanford CS147 [Materials](http://web.stanford.edu/class/cs124/)\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)\r\n\r\n### Data Design\r\n\r\n* **Visualization**\r\n\r\n _Foundational Information Design Books_\r\n\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n\r\n _Theoretical Courses / Design & Visualization_\r\n\r\n * Data Visualization [University of Washington / Slides & Resources](http://bit.ly/uw-dataviz)\r\n * Berkeley's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University / Slides](http://bit.ly/riceu-viz)\r\n\r\n _Practical Visualization Resources_\r\n\r\n * D3 Library / Scott Murray [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Interactive Data Visualization for the Web / Scott Murray [Online Book](bit.ly/interactive-data-viz-web) & [Book `$26`](http://amzn.to/1oK1xCN)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n_More Libraries can be found in related [specialiaztions](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [matplotlib](http://www.ast.uct.ac.za/~sarblyth/pythonGuide/PythonPlottingBeginnersGuide.pdf) - well-integrated with analysis and data manipulation packages like numpy and pandas\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Data Science as a Profession\r\n\r\n * Doing Data Science: Straight Talk from the Frontline [O'Reilly / Book ```$25```](http://amzn.to/1vAIscK)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nNon-Open-Source books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file From 25b298bc6227191a325447c152fb6736e82de065 Mon Sep 17 00:00:00 2001 From: Clare Date: Wed, 22 Oct 2014 18:04:48 -0700 Subject: [PATCH 31/35] Create gh-pages branch via GitHub --- index.html | 2 ++ params.json | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/index.html b/index.html index 60f1f2d2..3c22caa3 100644 --- a/index.html +++ b/index.html @@ -165,6 +165,8 @@

      Computing

      +

      Get your environment up and running with the Data Science Toolbox

      +

      • Algorithms

        diff --git a/params.json b/params.json index 97e14a98..d288ba3a 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"The Open Source Data Science Masters","tagline":"Curriculum for Data Science","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\nBuild cutting-edge Machine Learning applications with me at [Mattermark](http://www.mattermark.com/app/jobs) --> [email me!](http://www.google.com/recaptcha/mailhide/d?k=01KgESkAshOFOWRMyGkcdCEQ==&c=Cmjc2-OpRImyczz0qZ2fupPU5hPh_xmcRLiawYbBE8s=)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\nThe open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.\r\n\r\n### The Internet is Your Oyster\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book ```$27```](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n\r\n[★ What are some good resources for learning about numerical analysis? / Quora ]\r\n(http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)\r\n\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Online Course](http://bit.ly/stanford-introdatabases)\r\n * SQL School [Mode Analytics / Tutorials](http://sqlschool.modeanalytics.com/)\r\n * SQL Tutorials [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets / Stanford [Coursera](https://www.coursera.org/course/mmds) & [Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n\r\n _Foundational & Theoretical_\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn) & [Book ```$80```](http://amzn.to/1hmyKry) & [Study Group](http://www.reddit.com/r/eosl)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n\r\n _Practical_\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * Machine Learning for Hackers [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)\r\n * Intro to scikit-learn, SciPy2013 [youtube tutorials](http://bit.ly/scikit-video-tuts)\r\n\r\n* **Probabilistic Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n\r\n* **Deep Learning (Neural Networks)**\r\n * Neural Networks [Andrej Karpathy / Python Walkthrough](http://karpathy.github.io/neuralnets/)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Social Network & Graph Analysis**\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)\r\n\r\n* **Natural Language Processing**\r\n * From Languages to Information / Stanford CS147 [Materials](http://web.stanford.edu/class/cs124/)\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)\r\n\r\n### Data Design\r\n\r\n* **Visualization**\r\n\r\n _Foundational Information Design Books_\r\n\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n\r\n _Theoretical Courses / Design & Visualization_\r\n\r\n * Data Visualization [University of Washington / Slides & Resources](http://bit.ly/uw-dataviz)\r\n * Berkeley's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University / Slides](http://bit.ly/riceu-viz)\r\n\r\n _Practical Visualization Resources_\r\n\r\n * D3 Library / Scott Murray [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Interactive Data Visualization for the Web / Scott Murray [Online Book](bit.ly/interactive-data-viz-web) & [Book `$26`](http://amzn.to/1oK1xCN)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n_More Libraries can be found in related [specialiaztions](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [matplotlib](http://www.ast.uct.ac.za/~sarblyth/pythonGuide/PythonPlottingBeginnersGuide.pdf) - well-integrated with analysis and data manipulation packages like numpy and pandas\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Data Science as a Profession\r\n\r\n * Doing Data Science: Straight Talk from the Frontline [O'Reilly / Book ```$25```](http://amzn.to/1vAIscK)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nNon-Open-Source books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The Open Source Data Science Masters","tagline":"Curriculum for Data Science","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\nBuild cutting-edge Machine Learning applications with me at [Mattermark](http://www.mattermark.com/app/jobs) --> [email me!](http://www.google.com/recaptcha/mailhide/d?k=01KgESkAshOFOWRMyGkcdCEQ==&c=Cmjc2-OpRImyczz0qZ2fupPU5hPh_xmcRLiawYbBE8s=)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\nThe open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.\r\n\r\n### The Internet is Your Oyster\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book ```$27```](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n\r\n[★ What are some good resources for learning about numerical analysis? / Quora ]\r\n(http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)\r\n\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n\r\nGet your environment up and running with the [Data Science Toolbox](http://datasciencetoolbox.org)\r\n\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Online Course](http://bit.ly/stanford-introdatabases)\r\n * SQL School [Mode Analytics / Tutorials](http://sqlschool.modeanalytics.com/)\r\n * SQL Tutorials [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets / Stanford [Coursera](https://www.coursera.org/course/mmds) & [Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n\r\n _Foundational & Theoretical_\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn) & [Book ```$80```](http://amzn.to/1hmyKry) & [Study Group](http://www.reddit.com/r/eosl)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n\r\n _Practical_\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * Machine Learning for Hackers [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)\r\n * Intro to scikit-learn, SciPy2013 [youtube tutorials](http://bit.ly/scikit-video-tuts)\r\n\r\n* **Probabilistic Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n\r\n* **Deep Learning (Neural Networks)**\r\n * Neural Networks [Andrej Karpathy / Python Walkthrough](http://karpathy.github.io/neuralnets/)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Social Network & Graph Analysis**\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)\r\n\r\n* **Natural Language Processing**\r\n * From Languages to Information / Stanford CS147 [Materials](http://web.stanford.edu/class/cs124/)\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)\r\n\r\n### Data Design\r\n\r\n* **Visualization**\r\n\r\n _Foundational Information Design Books_\r\n\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n\r\n _Theoretical Courses / Design & Visualization_\r\n\r\n * Data Visualization [University of Washington / Slides & Resources](http://bit.ly/uw-dataviz)\r\n * Berkeley's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University / Slides](http://bit.ly/riceu-viz)\r\n\r\n _Practical Visualization Resources_\r\n\r\n * D3 Library / Scott Murray [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Interactive Data Visualization for the Web / Scott Murray [Online Book](bit.ly/interactive-data-viz-web) & [Book `$26`](http://amzn.to/1oK1xCN)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n_More Libraries can be found in related [specialiaztions](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [matplotlib](http://www.ast.uct.ac.za/~sarblyth/pythonGuide/PythonPlottingBeginnersGuide.pdf) - well-integrated with analysis and data manipulation packages like numpy and pandas\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Data Science as a Profession\r\n\r\n * Doing Data Science: Straight Talk from the Frontline [O'Reilly / Book ```$25```](http://amzn.to/1vAIscK)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nNon-Open-Source books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file From db318a5b89f3fb8a02fa408a8f13601c5487f5fd Mon Sep 17 00:00:00 2001 From: Clare Date: Tue, 28 Oct 2014 23:01:49 -0700 Subject: [PATCH 32/35] Create gh-pages branch via GitHub --- index.html | 6 ++++-- params.json | 2 +- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/index.html b/index.html index 3c22caa3..c770a531 100644 --- a/index.html +++ b/index.html @@ -33,7 +33,7 @@

        Curriculum for Data Science

        Follow me on Twitter @clarecorthell

        -

        Build cutting-edge Machine Learning applications with me at Mattermark --> email me!

        +

        Build Machine Learning applications with us at Mattermark --> email me!

        The Open-Source Data Science Masters

        @@ -192,7 +192,7 @@

        Databases

          -
        • Introduction to Databases Stanford / Online Course +
        • Introduction to Databases Stanford / Online Course
        • SQL School Mode Analytics / Tutorials
          • @@ -487,6 +487,8 @@

        • Zipfian Academy's List of Resources
        • A Software Engineer's Guide to Getting Started with Data Science
          • +
        • Josh Wills - The Life of a Data Scientist / Video +
        • Data Scientist Interviews Metamarkets
        • /r/MachineLearning Reddit diff --git a/params.json b/params.json index d288ba3a..9ee9e9f3 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"The Open Source Data Science Masters","tagline":"Curriculum for Data Science","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\nBuild cutting-edge Machine Learning applications with me at [Mattermark](http://www.mattermark.com/app/jobs) --> [email me!](http://www.google.com/recaptcha/mailhide/d?k=01KgESkAshOFOWRMyGkcdCEQ==&c=Cmjc2-OpRImyczz0qZ2fupPU5hPh_xmcRLiawYbBE8s=)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\nThe open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.\r\n\r\n### The Internet is Your Oyster\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book ```$27```](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n\r\n[★ What are some good resources for learning about numerical analysis? / Quora ]\r\n(http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)\r\n\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n\r\nGet your environment up and running with the [Data Science Toolbox](http://datasciencetoolbox.org)\r\n\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Online Course](http://bit.ly/stanford-introdatabases)\r\n * SQL School [Mode Analytics / Tutorials](http://sqlschool.modeanalytics.com/)\r\n * SQL Tutorials [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets / Stanford [Coursera](https://www.coursera.org/course/mmds) & [Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n\r\n _Foundational & Theoretical_\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn) & [Book ```$80```](http://amzn.to/1hmyKry) & [Study Group](http://www.reddit.com/r/eosl)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n\r\n _Practical_\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * Machine Learning for Hackers [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)\r\n * Intro to scikit-learn, SciPy2013 [youtube tutorials](http://bit.ly/scikit-video-tuts)\r\n\r\n* **Probabilistic Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n\r\n* **Deep Learning (Neural Networks)**\r\n * Neural Networks [Andrej Karpathy / Python Walkthrough](http://karpathy.github.io/neuralnets/)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Social Network & Graph Analysis**\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)\r\n\r\n* **Natural Language Processing**\r\n * From Languages to Information / Stanford CS147 [Materials](http://web.stanford.edu/class/cs124/)\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)\r\n\r\n### Data Design\r\n\r\n* **Visualization**\r\n\r\n _Foundational Information Design Books_\r\n\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n\r\n _Theoretical Courses / Design & Visualization_\r\n\r\n * Data Visualization [University of Washington / Slides & Resources](http://bit.ly/uw-dataviz)\r\n * Berkeley's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University / Slides](http://bit.ly/riceu-viz)\r\n\r\n _Practical Visualization Resources_\r\n\r\n * D3 Library / Scott Murray [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Interactive Data Visualization for the Web / Scott Murray [Online Book](bit.ly/interactive-data-viz-web) & [Book `$26`](http://amzn.to/1oK1xCN)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n_More Libraries can be found in related [specialiaztions](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [matplotlib](http://www.ast.uct.ac.za/~sarblyth/pythonGuide/PythonPlottingBeginnersGuide.pdf) - well-integrated with analysis and data manipulation packages like numpy and pandas\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Data Science as a Profession\r\n\r\n * Doing Data Science: Straight Talk from the Frontline [O'Reilly / Book ```$25```](http://amzn.to/1vAIscK)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nNon-Open-Source books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The Open Source Data Science Masters","tagline":"Curriculum for Data Science","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\nBuild Machine Learning applications with us at [Mattermark](http://www.mattermark.com/app/jobs) --> [email me!](http://www.google.com/recaptcha/mailhide/d?k=01KgESkAshOFOWRMyGkcdCEQ==&c=Cmjc2-OpRImyczz0qZ2fupPU5hPh_xmcRLiawYbBE8s=)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\nThe open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.\r\n\r\n### The Internet is Your Oyster\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book ```$27```](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n\r\n[★ What are some good resources for learning about numerical analysis? / Quora ]\r\n(http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)\r\n\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n\r\nGet your environment up and running with the [Data Science Toolbox](http://datasciencetoolbox.org)\r\n\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Online Course](https://bit.ly/introdatabases)\r\n * SQL School [Mode Analytics / Tutorials](http://sqlschool.modeanalytics.com/)\r\n * SQL Tutorials [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets / Stanford [Coursera](https://www.coursera.org/course/mmds) & [Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n\r\n _Foundational & Theoretical_\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn) & [Book ```$80```](http://amzn.to/1hmyKry) & [Study Group](http://www.reddit.com/r/eosl)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n\r\n _Practical_\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * Machine Learning for Hackers [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)\r\n * Intro to scikit-learn, SciPy2013 [youtube tutorials](http://bit.ly/scikit-video-tuts)\r\n\r\n* **Probabilistic Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n\r\n* **Deep Learning (Neural Networks)**\r\n * Neural Networks [Andrej Karpathy / Python Walkthrough](http://karpathy.github.io/neuralnets/)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Social Network & Graph Analysis**\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)\r\n\r\n* **Natural Language Processing**\r\n * From Languages to Information / Stanford CS147 [Materials](http://web.stanford.edu/class/cs124/)\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)\r\n\r\n### Data Design\r\n\r\n* **Visualization**\r\n\r\n _Foundational Information Design Books_\r\n\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n\r\n _Theoretical Courses / Design & Visualization_\r\n\r\n * Data Visualization [University of Washington / Slides & Resources](http://bit.ly/uw-dataviz)\r\n * Berkeley's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University / Slides](http://bit.ly/riceu-viz)\r\n\r\n _Practical Visualization Resources_\r\n\r\n * D3 Library / Scott Murray [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Interactive Data Visualization for the Web / Scott Murray [Online Book](bit.ly/interactive-data-viz-web) & [Book `$26`](http://amzn.to/1oK1xCN)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n_More Libraries can be found in related [specialiaztions](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [matplotlib](http://www.ast.uct.ac.za/~sarblyth/pythonGuide/PythonPlottingBeginnersGuide.pdf) - well-integrated with analysis and data manipulation packages like numpy and pandas\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Data Science as a Profession\r\n\r\n * Doing Data Science: Straight Talk from the Frontline [O'Reilly / Book ```$25```](http://amzn.to/1vAIscK)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Josh Wills - The Life of a Data Scientist / [Video](https://www.youtube.com/watch?v=h9vQIPfe2uU)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nNon-Open-Source books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file From 8d6f3aff62d57dc9396121d44082897e055afc21 Mon Sep 17 00:00:00 2001 From: Clare Date: Fri, 6 Feb 2015 14:29:38 -0800 Subject: [PATCH 33/35] Create gh-pages branch via GitHub --- index.html | 51 ++++++++++++++++++++++---------------- params.json | 2 +- stylesheets/stylesheet.css | 6 +++-- 3 files changed, 35 insertions(+), 24 deletions(-) diff --git a/index.html b/index.html index c770a531..b531f5d8 100644 --- a/index.html +++ b/index.html @@ -33,20 +33,22 @@

          Curriculum for Data Science

          Follow me on Twitter @clarecorthell

          -

          Build Machine Learning applications with us at Mattermark --> email me!

          +

          Email me at github@clarecorthell.com

          + +

          (psst - Mattermark is hiring)

          -The Open-Source Data Science Masters

          +The Open-Source Data Science Masters

          • The open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.

          -The Internet is Your Oyster

          +The Internet is Your Oyster

        With Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?

        -The Motivation

        +The Motivation

    • We need more Data Scientists.

    @@ -63,7 +65,7 @@

    -- David Hardtke How To Hire A Data Scientist 13 Nov 2012

    -An Academic Shortfall

    +An Academic Shortfall

    Classic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.

    @@ -78,12 +80,12 @@

    -- James Kobielus, Closing the Talent Gap 17 Jan 2013

    -Ready?

    +Ready?

    -The Open Source Data Science Curriculum

    +The Open Source Data Science Curriculum

    Start here. Intro to Data Science UW / Coursera

    @@ -110,13 +112,13 @@

    -A Note About Direction

    +A Note About Direction

    This is an introduction geared toward those with at least a minimum understanding of programming, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing). Out of personal preference and need for focus, I geared the original curriculum toward Python tools and resources. R resources can be found here.

    -Math

    +Math

    ★ What are some good resources for learning about numerical analysis? / Quora

    @@ -163,7 +165,7 @@

    -Computing

    +Computing

    Get your environment up and running with the Data Science Toolbox

    @@ -299,7 +301,7 @@

    -Data Design

    +Data Design
    • @@ -339,7 +341,7 @@

      OSDSM Specialization: Data Journalism

      - + Python (Learning)

        @@ -354,12 +356,17 @@

      - + Python (Libraries)

      Installing Basic Packages Python, virtualenv, NumPy, SciPy, matplotlib and IPython & Using Python Scientifically

      -

      More Libraries can be found in related specialiaztions

      + + +

      More Libraries can be found in related specializations

      • @@ -441,15 +448,15 @@

      -Datasets are now here +Datasets are now here

      -R resources are now here +R resources are now here

      -Data Science as a Profession

      +Data Science as a Profession
      • Doing Data Science: Straight Talk from the Frontline O'Reilly / Book $25 @@ -457,18 +464,20 @@

      -Capstone Project

      +Capstone Project

      -Resources

      +Resources
      • @@ -498,12 +507,12 @@

        -Notation

        +Notation

        Non-Open-Source books, courses, and resources are noted with $.

        -Contribute

        +Contribute

        Please Contribute Your Ideas -- this is Open Source!

        diff --git a/params.json b/params.json index 9ee9e9f3..7421f771 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"The Open Source Data Science Masters","tagline":"Curriculum for Data Science","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\nBuild Machine Learning applications with us at [Mattermark](http://www.mattermark.com/app/jobs) --> [email me!](http://www.google.com/recaptcha/mailhide/d?k=01KgESkAshOFOWRMyGkcdCEQ==&c=Cmjc2-OpRImyczz0qZ2fupPU5hPh_xmcRLiawYbBE8s=)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\nThe open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.\r\n\r\n### The Internet is Your Oyster\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book ```$27```](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n\r\n[★ What are some good resources for learning about numerical analysis? / Quora ]\r\n(http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)\r\n\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n\r\nGet your environment up and running with the [Data Science Toolbox](http://datasciencetoolbox.org)\r\n\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Online Course](https://bit.ly/introdatabases)\r\n * SQL School [Mode Analytics / Tutorials](http://sqlschool.modeanalytics.com/)\r\n * SQL Tutorials [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets / Stanford [Coursera](https://www.coursera.org/course/mmds) & [Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n\r\n _Foundational & Theoretical_\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn) & [Book ```$80```](http://amzn.to/1hmyKry) & [Study Group](http://www.reddit.com/r/eosl)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n\r\n _Practical_\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * Machine Learning for Hackers [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)\r\n * Intro to scikit-learn, SciPy2013 [youtube tutorials](http://bit.ly/scikit-video-tuts)\r\n\r\n* **Probabilistic Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n\r\n* **Deep Learning (Neural Networks)**\r\n * Neural Networks [Andrej Karpathy / Python Walkthrough](http://karpathy.github.io/neuralnets/)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Social Network & Graph Analysis**\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)\r\n\r\n* **Natural Language Processing**\r\n * From Languages to Information / Stanford CS147 [Materials](http://web.stanford.edu/class/cs124/)\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)\r\n\r\n### Data Design\r\n\r\n* **Visualization**\r\n\r\n _Foundational Information Design Books_\r\n\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n\r\n _Theoretical Courses / Design & Visualization_\r\n\r\n * Data Visualization [University of Washington / Slides & Resources](http://bit.ly/uw-dataviz)\r\n * Berkeley's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University / Slides](http://bit.ly/riceu-viz)\r\n\r\n _Practical Visualization Resources_\r\n\r\n * D3 Library / Scott Murray [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Interactive Data Visualization for the Web / Scott Murray [Online Book](bit.ly/interactive-data-viz-web) & [Book `$26`](http://amzn.to/1oK1xCN)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n_More Libraries can be found in related [specialiaztions](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [matplotlib](http://www.ast.uct.ac.za/~sarblyth/pythonGuide/PythonPlottingBeginnersGuide.pdf) - well-integrated with analysis and data manipulation packages like numpy and pandas\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Data Science as a Profession\r\n\r\n * Doing Data Science: Straight Talk from the Frontline [O'Reilly / Book ```$25```](http://amzn.to/1vAIscK)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Josh Wills - The Life of a Data Scientist / [Video](https://www.youtube.com/watch?v=h9vQIPfe2uU)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nNon-Open-Source books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The Open Source Data Science Masters","tagline":"Curriculum for Data Science","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\nEmail me at [github@clarecorthell.com](mailto:github@clarecorthell.com)\r\n\r\n(psst - [Mattermark is hiring](http://www.mattermark.com/app/jobs))\r\n\r\n## The Open-Source Data Science Masters\r\n\r\nThe open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.\r\n\r\n### The Internet is Your Oyster\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book ```$27```](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n\r\n[★ What are some good resources for learning about numerical analysis? / Quora ]\r\n(http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)\r\n\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n\r\nGet your environment up and running with the [Data Science Toolbox](http://datasciencetoolbox.org)\r\n\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Online Course](https://bit.ly/introdatabases)\r\n * SQL School [Mode Analytics / Tutorials](http://sqlschool.modeanalytics.com/)\r\n * SQL Tutorials [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets / Stanford [Coursera](https://www.coursera.org/course/mmds) & [Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n\r\n _Foundational & Theoretical_\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn) & [Book ```$80```](http://amzn.to/1hmyKry) & [Study Group](http://www.reddit.com/r/eosl)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n\r\n _Practical_\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * Machine Learning for Hackers [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)\r\n * Intro to scikit-learn, SciPy2013 [youtube tutorials](http://bit.ly/scikit-video-tuts)\r\n\r\n* **Probabilistic Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n\r\n* **Deep Learning (Neural Networks)**\r\n * Neural Networks [Andrej Karpathy / Python Walkthrough](http://karpathy.github.io/neuralnets/)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Social Network & Graph Analysis**\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)\r\n\r\n* **Natural Language Processing**\r\n * From Languages to Information / Stanford CS147 [Materials](http://web.stanford.edu/class/cs124/)\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)\r\n\r\n### Data Design\r\n\r\n* **Visualization**\r\n\r\n _Foundational Information Design Books_\r\n\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n\r\n _Theoretical Courses / Design & Visualization_\r\n\r\n * Data Visualization [University of Washington / Slides & Resources](http://bit.ly/uw-dataviz)\r\n * Berkeley's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University / Slides](http://bit.ly/riceu-viz)\r\n\r\n _Practical Visualization Resources_\r\n\r\n * D3 Library / Scott Murray [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Interactive Data Visualization for the Web / Scott Murray [Online Book](bit.ly/interactive-data-viz-web) & [Book `$26`](http://amzn.to/1oK1xCN)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n * [Pandas Cookbook](https://github.com/jvns/pandas-cookbook) (data structure library)\r\n\r\n_More Libraries can be found in related [specializations](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [matplotlib](http://www.ast.uct.ac.za/~sarblyth/pythonGuide/PythonPlottingBeginnersGuide.pdf) - well-integrated with analysis and data manipulation packages like numpy and pandas\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Data Science as a Profession\r\n\r\n * Doing Data Science: Straight Talk from the Frontline [O'Reilly / Book ```$25```](http://amzn.to/1vAIscK)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n* Analyze your LinkedIn Network [Generate & Download Adjacency Matrix](http://socilab.com/)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Josh Wills - The Life of a Data Scientist / [Video](https://www.youtube.com/watch?v=h9vQIPfe2uU)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nNon-Open-Source books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file diff --git a/stylesheets/stylesheet.css b/stylesheets/stylesheet.css index 7a08b019..619d19d7 100644 --- a/stylesheets/stylesheet.css +++ b/stylesheets/stylesheet.css @@ -201,12 +201,14 @@ ul, ol, dl { } ul { - list-style: inside; + list-style-position: inside; + list-style: disc; padding-left: 20px; } ol { - list-style: decimal inside; + list-style-position: inside; + list-style: decimal; padding-left: 20px; } From 008102033241560a8fd0fdf3690b9f8f66c0d576 Mon Sep 17 00:00:00 2001 From: Clare Date: Sun, 8 Mar 2015 21:19:53 -0700 Subject: [PATCH 34/35] Create gh-pages branch via GitHub --- index.html | 10 +++++----- params.json | 2 +- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/index.html b/index.html index b531f5d8..9027d7f6 100644 --- a/index.html +++ b/index.html @@ -33,10 +33,6 @@

        Curriculum for Data Science

        Follow me on Twitter @clarecorthell

        -

        Email me at github@clarecorthell.com

        - -

        (psst - Mattermark is hiring)

        -

        The Open-Source Data Science Masters

        @@ -290,7 +286,7 @@

        Analysis

        diff --git a/params.json b/params.json index 7421f771..48700378 100644 --- a/params.json +++ b/params.json @@ -1 +1 @@ -{"name":"The Open Source Data Science Masters","tagline":"Curriculum for Data Science","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\nEmail me at [github@clarecorthell.com](mailto:github@clarecorthell.com)\r\n\r\n(psst - [Mattermark is hiring](http://www.mattermark.com/app/jobs))\r\n\r\n## The Open-Source Data Science Masters\r\n\r\nThe open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.\r\n\r\n### The Internet is Your Oyster\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book ```$27```](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n\r\n[★ What are some good resources for learning about numerical analysis? / Quora ]\r\n(http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)\r\n\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n\r\nGet your environment up and running with the [Data Science Toolbox](http://datasciencetoolbox.org)\r\n\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Online Course](https://bit.ly/introdatabases)\r\n * SQL School [Mode Analytics / Tutorials](http://sqlschool.modeanalytics.com/)\r\n * SQL Tutorials [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets / Stanford [Coursera](https://www.coursera.org/course/mmds) & [Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n\r\n _Foundational & Theoretical_\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn) & [Book ```$80```](http://amzn.to/1hmyKry) & [Study Group](http://www.reddit.com/r/eosl)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n\r\n _Practical_\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * Machine Learning for Hackers [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)\r\n * Intro to scikit-learn, SciPy2013 [youtube tutorials](http://bit.ly/scikit-video-tuts)\r\n\r\n* **Probabilistic Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n\r\n* **Deep Learning (Neural Networks)**\r\n * Neural Networks [Andrej Karpathy / Python Walkthrough](http://karpathy.github.io/neuralnets/)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Social Network & Graph Analysis**\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)\r\n\r\n* **Natural Language Processing**\r\n * From Languages to Information / Stanford CS147 [Materials](http://web.stanford.edu/class/cs124/)\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Paper Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)\r\n\r\n### Data Design\r\n\r\n* **Visualization**\r\n\r\n _Foundational Information Design Books_\r\n\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n\r\n _Theoretical Courses / Design & Visualization_\r\n\r\n * Data Visualization [University of Washington / Slides & Resources](http://bit.ly/uw-dataviz)\r\n * Berkeley's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University / Slides](http://bit.ly/riceu-viz)\r\n\r\n _Practical Visualization Resources_\r\n\r\n * D3 Library / Scott Murray [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Interactive Data Visualization for the Web / Scott Murray [Online Book](bit.ly/interactive-data-viz-web) & [Book `$26`](http://amzn.to/1oK1xCN)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n * [Pandas Cookbook](https://github.com/jvns/pandas-cookbook) (data structure library)\r\n\r\n_More Libraries can be found in related [specializations](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [matplotlib](http://www.ast.uct.ac.za/~sarblyth/pythonGuide/PythonPlottingBeginnersGuide.pdf) - well-integrated with analysis and data manipulation packages like numpy and pandas\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Data Science as a Profession\r\n\r\n * Doing Data Science: Straight Talk from the Frontline [O'Reilly / Book ```$25```](http://amzn.to/1vAIscK)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n* Analyze your LinkedIn Network [Generate & Download Adjacency Matrix](http://socilab.com/)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Josh Wills - The Life of a Data Scientist / [Video](https://www.youtube.com/watch?v=h9vQIPfe2uU)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nNon-Open-Source books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file +{"name":"The Open Source Data Science Masters","tagline":"Curriculum for Data Science","body":"[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\n## The Open-Source Data Science Masters\r\n\r\nThe open-source curriculum for learning Data Science. Foundational in both theory and technologies, the OSDSM breaks down the core competencies necessary to make data useful.\r\n\r\n### The Internet is Your Oyster\r\n\r\nWith Coursera, ebooks, Stack Overflow, and GitHub -- all free and open -- how can you afford not to take advantage of an open source education?\r\n\r\n### The Motivation\r\n\r\nWe need more Data Scientists.\r\n\r\n> ...by 2018 the United States will experience a shortage of 190,000 skilled data scientists, and 1.5 million managers and analysts capable of reaping actionable insights from the big data deluge.\r\n\r\n-- [McKinsey Report Highlights the Impending Data Scientist Shortage](http://bit.ly/datascienceshortage) 23 July 2013\r\n\r\n> There are little to no Data Scientists with 5 years experience, because the job simply did not exist.\r\n\r\n-- David Hardtke [How To Hire A Data Scientist](http://bit.ly/howtohireadatascientist) 13 Nov 2012\r\n\r\n### An Academic Shortfall\r\n\r\nClassic academic conduits aren't providing Data Scientists -- this talent gap will be closed differently.\r\n\r\n> **Academic credentials are important but not necessary for high-quality data science.** The core aptitudes – curiosity, intellectual agility, statistical fluency, research stamina, scientific rigor, skeptical nature – that distinguish the best data scientists are widely distributed throughout the population.\r\n\r\n> We’re likely to see more uncredentialed, inexperienced individuals try their hands at data science, **bootstrapping their skills on the open-source ecosystem and using the diversity of modeling tools available.** Just as data-science platforms and tools are proliferating through the magic of open source, big data’s data-scientist pool will as well.\r\n\r\n> And there’s yet another trend that will alleviate any talent gap: the democratization of data science. While I agree wholeheartedly with Raden’s statement that “the crème-de-la-crème of data scientists will fill roles in academia, technology vendors, Wall Street, research and government,” I think he’s understating the extent to which **autodidacts – the self-taught, uncredentialed, data-passionate people – will come to play a significant role in many organizations’ data science initiatives.**\r\n\r\n-- James Kobielus, [Closing the Talent Gap](http://bit.ly/closingthetalentgap) 17 Jan 2013\r\n\r\n### Ready?\r\n\r\n***\r\n\r\n## The Open Source Data Science Curriculum\r\n\r\nStart here.\r\n**Intro to Data Science** [UW / Coursera](http://bit.ly/uwintrodatascience)\r\n * *Topics:* Python NLP on Twitter API, Distributed Computing Paradigm, MapReduce/Hadoop & Pig Script, SQL/NoSQL, Relational Algebra, Experiment design, Statistics, Graphs, Amazon EC2, Visualization.\r\n\r\n**Data Science** / Harvard [Video Archive](http://bit.ly/harvarddatasciencevideos) & [Course](http://bit.ly/harvarddatasciencecourse)\r\n * *Topics:* Data wrangling, data management, exploratory data analysis to generate hypotheses and intuition, prediction based on statistical methods such as regression and classification, communication of results through visualization, stories, and summaries.\r\n\r\n**Data Science with Open Source Tools** [Book ```$27```](http://bit.ly/book-datasciencewithopensourcetools)\r\n * *Topics:* Visualizing Data, Estimation, Models from Scaling Arguments, Arguments from Probability Models, What you Really Need to Know about Classical Statistics, Data Mining, Clustering, PCA, Map/Reduce, Predictive Analytics\r\n * *Example Code in:* R, Python, Sage, C, Gnu Scientific Library\r\n\r\n### A Note About Direction\r\nThis is an introduction geared toward those with at least **a minimum understanding of programming**, and (perhaps obviously) an interest in the components of Data Science (like statistics and distributed computing).\r\nOut of personal preference and need for focus, I geared the original curriculum toward **Python tools and resources**. R resources can be found [here](http://bit.ly/osdsm-rresources).\r\n\r\n### Math\r\n\r\n[★ What are some good resources for learning about numerical analysis? / Quora ]\r\n(http://www.quora.com/What-are-some-good-resources-for-learning-about-numerical-analysis)\r\n\r\n* **Linear Algebra & Programming**\r\n * Linear Algebra / Levandosky [Stanford / Book ```$10```](http://amzn.to/1kIfmmI)\r\n * Linear Programming (Math 407) [University of Washington / Course](http://bit.ly/course-uw-linearprogramming)\r\n\r\n* **Statistics**\r\n * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) \r\n * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X)\r\n * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf)\r\n * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr)\r\n\r\n* **Differential Equations & Calculus**\r\n * Differential Equations in Data Science [Python Tutorial](http://bit.ly/ipynb-differentialeq)\r\n\r\n* **Problem Solving**\r\n * Problem-Solving Heuristics \"How To Solve It\" [Polya / Book ```$10```](http://amzn.to/1mqJRSi)\r\n\r\n### Computing\r\n\r\nGet your environment up and running with the [Data Science Toolbox](http://datasciencetoolbox.org)\r\n\r\n* **Algorithms**\r\n * Algorithms Design & Analysis I [Stanford / Coursera](http://bit.ly/coursera-algo)\r\n * Algorithm Design, Kleinberg & Tardos [Book ```$125```](http://amzn.to/1iMnWm5)\r\n\r\n* **Distributed Computing Paradigms**\r\n * *See Intro to Data Science [UW / Lectures on MapReduce](http://bit.ly/uwintrodatascience)\r\n * Intro to Hadoop and MapReduce [Cloudera / Udacity Course](http://bit.ly/udacity-hadoopmapreduce) *includes select free excerpts of Hadoop: The Definitive Guide [Book ```$29```](http://amzn.to/1i7wgLv)\r\n\r\n* **Databases**\r\n * Introduction to Databases [Stanford / Online Course](https://bit.ly/introdatabases)\r\n * SQL School [Mode Analytics / Tutorials](http://sqlschool.modeanalytics.com/)\r\n * SQL Tutorials [SQLZOO / Tutorials](http://bit.ly/tut-sqlzoo)\r\n\r\n* **Data Mining**\r\n * Mining Massive Data Sets / Stanford [Coursera](https://www.coursera.org/course/mmds) & [Digital](http://bit.ly/ebook-miningmassivedata) & [Book ```$58```](http://amzn.to/1txocpo)\r\n * Mining The Social Web [Book ```$30```](http://amzn.to/1mqxAsB)\r\n * Introduction to Information Retrieval / Stanford [Digital](http://bit.ly/ebook-stanford-inforetrieval) & [Book ```$56```](http://amzn.to/1mWbnUT)\r\n\r\n_OSDSM Specialization: [Web Scraping & Crawling](https://github.com/datasciencemasters/go/blob/master/specializations.md#web-scraping--crawling)_\r\n\r\n* **Machine Learning**\r\n\r\n _Foundational & Theoretical_\r\n * Machine Learning [Ng Stanford / Coursera](http://bit.ly/stanford-ml)\r\n * A Course in Machine Learning [UMD / Digital Book](http://ciml.info/)\r\n * The Elements of Statistical Learning / Stanford [Digital](http://bit.ly/ebook-elemstatlearn) & [Book ```$80```](http://amzn.to/1hmyKry) & [Study Group](http://www.reddit.com/r/eosl)\r\n * Machine Learning [Caltech / Edx](http://bit.ly/caltech-ml)\r\n\r\n _Practical_\r\n * Programming Collective Intelligence [Book ```$27```](http://amzn.to/1mqxYqW)\r\n * Machine Learning for Hackers [ipynb / digital book](http://nbviewer.ipython.org/github/carljv/Will_it_Python/blob/master/MLFH/CH1/chapter1.ipynb)\r\n * Intro to scikit-learn, SciPy2013 [youtube tutorials](http://bit.ly/scikit-video-tuts)\r\n\r\n* **Probabilistic Modeling**\r\n * Probabilistic Programming and Bayesian Methods for Hackers [Github / Tutorials](http://bit.ly/ipnb-probabilisticprogramming)\r\n * Probabalistic Graphical Models [Stanford / Coursera](http://bit.ly/stanford-pgm)\r\n\r\n* **Deep Learning (Neural Networks)**\r\n * Neural Networks [Andrej Karpathy / Python Walkthrough](http://karpathy.github.io/neuralnets/)\r\n * Neural Networks [U Toronto / Coursera](http://bit.ly/utoronto-neuralnets)\r\n\r\n* **Social Network & Graph Analysis**\r\n * Social and Economic Networks: Models and Analysis / [Stanford / Coursera](http://bit.ly/stanford-socialeconnetworks)\r\n * Social Network Analysis for Startups [Book ```$22```](http://amzn.to/1jySCCT)\r\n\r\n* **Natural Language Processing**\r\n * From Languages to Information / Stanford CS147 [Materials](http://web.stanford.edu/class/cs124/)\r\n * NLP with Python (NLTK library) [Digital](http://bit.ly/ebook-nltk), [Book ```$36```](http://amzn.to/1iMrDIp)\r\n\r\n* **Analysis**\r\n * Python for Data Analysis [Book ```$24```](http://amzn.to/Q2pI5I)\r\n * Big Data Analysis with Twitter [UC Berkeley / Lectures](http://bit.ly/cal-course-bigdatatwitter)\r\n * Exploratory Data Analysis [Tukey / Book ```$81```](http://amzn.to/1kNUEPa)\r\n\r\n### Data Design\r\n\r\n* **Visualization**\r\n\r\n _Foundational Information Design Books_\r\n\r\n * Envisioning Information [Tufte / Book ```$36```](http://amzn.to/Sn0QI4)\r\n * The Visual Display of Quantitative Information [Tufte / Book ```$27```](http://amzn.to/1q5FB91)\r\n\r\n _Theoretical Courses / Design & Visualization_\r\n\r\n * Data Visualization [University of Washington / Slides & Resources](http://bit.ly/uw-dataviz)\r\n * Berkeley's Viz Class [UC Berkeley / Course Docs](http://bit.ly/cal-viz)\r\n * Rice University's Data Viz class [Rice University / Slides](http://bit.ly/riceu-viz)\r\n\r\n _Practical Visualization Resources_\r\n\r\n * D3 Library / Scott Murray [Blog / Tutorials](http://bit.ly/tut-scottmurray-d3)\r\n * Interactive Data Visualization for the Web / Scott Murray [Online Book](bit.ly/interactive-data-viz-web) & [Book `$26`](http://amzn.to/1oK1xCN)\r\n\r\n_OSDSM Specialization: [Data Journalism](https://github.com/datasciencemasters/go/blob/master/specializations.md#data-journalism)_\r\n\r\n#### **Python** (Learning)\r\n * Learn Python the Hard Way [Digital](http://bit.ly/ebook-learnpyhardway) & [Book ```$23```](http://amzn.to/1hmzGw9)\r\n * Python [Class / Google](http://bit.ly/T4j40A)\r\n * Think Python [Digital](http://bit.ly/ebook-thinkpy) & [Book ```$34```](http://amzn.to/1ktQ5ZU)\r\n * Introduction to Computer Science and Programming [MIT OpenCourseWare / Lectures](http://bit.ly/mit-cs)\r\n\r\n#### **Python** (Libraries)\r\nInstalling Basic Packages [Python, virtualenv, NumPy, SciPy, matplotlib and IPython ](http://bit.ly/scientific-py-install) & [Using Python Scientifically](http://bit.ly/lecture-scipy)\r\n\r\n[Command Line Install Script](https://github.com/fonnesbeck/ScipySuperpack) for Scientific Python Packages\r\n\r\n * [Pandas Cookbook](https://github.com/jvns/pandas-cookbook) (data structure library)\r\n\r\n_More Libraries can be found in related [specializations](https://github.com/datasciencemasters/go/blob/master/specializations.md)_\r\n\r\n * **Data Structures & Analysis Packages**\r\n * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I)\r\n\r\n * **Machine Learning Packages**\r\n * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis \r\n\r\n * **Networks Packages**\r\n * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz \r\n\r\n * **Statistical Packages**\r\n * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit\r\n * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests\r\n * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python \r\n\r\n * **Natural Language Processing & Understanding**\r\n * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit \r\n * [Gensim](http://bit.ly/py-gensim) - Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.\r\n\r\n * **Live Data Packages**\r\n * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API \r\n\r\n * **Visualization Packages**\r\n * [matplotlib](http://www.ast.uct.ac.za/~sarblyth/pythonGuide/PythonPlottingBeginnersGuide.pdf) - well-integrated with analysis and data manipulation packages like numpy and pandas\r\n * [Orange](http://bit.ly/software-orangeviz) - Open source data visualization and analysis for novice and experts. Data mining through visual programming or Python scripting. Components for machine learning. Add-ons for bioinformatics and text mining\r\n\r\n* **iPython Data Science Notebooks**\r\n * [Data Science in IPython Notebooks](http://bit.ly/ipynb-ds) (Linear Regression, Logistic Regression, Random Forests, K-Means Clustering)\r\n * [A Gallery of Interesting IPython Notebooks - Pandas for Data Analysis](https://github.com/ipython/ipython/wiki/A-gallery-of-interesting-IPython-Notebooks#pandas-for-data-analysis)\r\n\r\n#### Datasets are now [here](http://bit.ly/osdsm-datasets)\r\n\r\n#### R resources are now [here](http://bit.ly/osdsm-rresources)\r\n\r\n### Data Science as a Profession\r\n\r\n * Doing Data Science: Straight Talk from the Frontline [O'Reilly / Book ```$25```](http://amzn.to/1vAIscK)\r\n\r\n### Capstone Project\r\n* Capstone Analysis of Your Own Design; [Quora](http://bit.ly/quora-toyproblems)'s Idea Compendium\r\n* Healthcare Twitter Analysis [Coursolve & UW Data Science](http://bit.ly/project-healthcare-twitter-analysis)\r\n* Analyze your LinkedIn Network [Generate & Download Adjacency Matrix](http://socilab.com/)\r\n\r\n***\r\n### Resources\r\n\r\n* [DataTau](http://bit.ly/datatau) - The \"Hacker News\" of Data Science \r\n* [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn\r\n* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses \r\n* [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher\r\n* [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos\r\n* [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia\r\n* The Signal and The Noise - Nate Silver [Pop-Sci Data Analysis ```$15```](http://amzn.to/1hoxQoG)\r\n* [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We)\r\n* [A Software Engineer's Guide to Getting Started with Data Science](http://bit.ly/1jwgV4p)\r\n* Josh Wills - The Life of a Data Scientist / [Video](https://www.youtube.com/watch?v=h9vQIPfe2uU)\r\n* Data Scientist Interviews [Metamarkets](http://bit.ly/1r1tJot)\r\n* /r/MachineLearning [Reddit](http://bit.ly/1uANaEM)\r\n\r\n***\r\n\r\n### Notation\r\nNon-Open-Source books, courses, and resources are noted with ```$```.\r\n\r\n## Contribute\r\n\r\nPlease Contribute Your Ideas -- **this is Open Source!**\r\n\r\nPlease **showcase your own specialization & transcript** by submitting a markdown file pull request in the ```/transcripts``` directory with your name! eg [```clare-corthell-2014.md```](http://bit.ly/U6yVMU)\r\n\r\n[Follow me on Twitter @clarecorthell](http://bit.ly/clarecorthelltwitter)\r\n\r\nEmail me at [github@clarecorthell.com](mailto:github@clarecorthell.com)\r\n","google":"UA-43720588-1","note":"Don't delete this file! It's used internally to help with page regeneration."} \ No newline at end of file From 484bc013629c7d0aec36f1a06a1888c86b40965d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Florian=20B=C3=BCtow?= Date: Thu, 17 Mar 2016 15:52:12 +0100 Subject: [PATCH 35/35] Added missing link to the videos of Statistics I from Princeton/Coursera The video lectures from Statistics I are currently not available on coursera. However, they can be found on youtube. --- README.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 17c118db..b962b29a 100644 --- a/README.md +++ b/README.md @@ -68,7 +68,7 @@ Out of personal preference and need for focus, I geared the original curriculum * The Manga Guide to Linear Algebra [Book ```$19```](http://amzn.to/1n4hM5l) * **Statistics** - * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) + * Statistics I [Princeton / Coursera](http://bit.ly/course-princeton-stats) [Videos on YouTube] (http://bit.ly/1U9qvBh) * Stats in a Nutshell [Book ```$29```](http://amzn.to/1iMnx2X) * Think Stats: Probability and Statistics for Programmers [Digital](http://bit.ly/ebook-thinkstats) & [Book ```$25```](http://amzn.to/RcVnTf) * Think Bayes [Digital](http://bit.ly/ebook-thinkbayes) & [Book ```$25```](http://amzn.to/1hmy4Cr) @@ -179,22 +179,22 @@ _More Libraries can be found in the ["awesome machine learning"](https://github. * Flexible and powerful data analysis / manipulation library with labeled data structures objects, statistical functions, etc [pandas](http://bit.ly/py-pandas) & Tutorials [Python for Data Analysis / Book](http://amzn.to/Q2pI5I) * **Machine Learning Packages** - * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis + * [scikit-learn](http://bit.ly/py-scikit) - Tools for Data Mining & Analysis * **Networks Packages** - * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz + * [networkx](http://bit.ly/py-networkx) - Network Modeling & Viz * **Statistical Packages** * [PyMC](http://bit.ly/py-pymc) - Bayesian Inference & Markov Chain Monte Carlo sampling toolkit * [Statsmodels](http://bit.ly/py-statsmodel) - Python module that allows users to explore data, estimate statistical models, and perform statistical tests - * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python + * [PyMVPA](http://bit.ly/py-mvpa) - Multivariate Pattern Analysis in Python * **Natural Language Processing & Understanding** - * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit + * [NLTK](http://bit.ly/py-nltk) - Natural Language Toolkit * [Gensim](http://bit.ly/py-gensim) - Python library for topic modeling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community. * **Live Data Packages** - * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API + * [twython](http://bit.ly/py-twython) - Python wrapper for the Twitter API * **Visualization Packages** * [matplotlib](http://www.ast.uct.ac.za/~sarblyth/pythonGuide/PythonPlottingBeginnersGuide.pdf) - well-integrated with analysis and data manipulation packages like numpy and pandas @@ -222,7 +222,7 @@ _More Libraries can be found in the ["awesome machine learning"](https://github. ### Resources #### Read -* [DataTau](http://bit.ly/datatau) - The "Hacker News" of Data Science +* [DataTau](http://bit.ly/datatau) - The "Hacker News" of Data Science * [Wikipedia](http://bit.ly/1kKg0gD) - The free encyclopedia * [The Signal and The Noise - Nate Silver ```$15```](http://amzn.to/1hoxQoG) - Bestseller Pop Sci * [Zipfian Academy's List of Resources](http://bit.ly/1qoF1We) @@ -235,7 +235,7 @@ _More Libraries can be found in the ["awesome machine learning"](https://github. #### Learn * [Metacademy](http://bit.ly/metacademy) - Search for a concept you want to learn -* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses +* [Coursera](http://bit.ly/coursera-online-courses) - Online university courses * [Wolfram Alpha](http://bit.ly/wolframalpha-torus) - The smart number and info cruncher * [Khan Academy](http://bit.ly/khan-academy-lifeinsurance) - High quality, free learning videos