Better define target audience re: circulation dataset

[chennesy]

How could the content be improved?

Via Carpentries Lab review:

The data sets are all circulation data in csv format. I feel that in the setup and summary there should be a brief note that the lesson is geared towards those working with circulation data. I would also reword the other learner profiles to say that even though all the examples are for circulation they can learn the basics of python. There is mention of pymarc in the section on libraries; with the focus here very much on circ, I'm not sure it's worth mentioning here. You may want to call out those libraries that are used in the examples throughout rather than pymarc.

Episode 12: The focus is circulation data thoughout the lesson. However this is a Python intro for libraries. I realize that it is difficult to address all types of data used in libraries. With that in mind, I wonder if there should be some sort of scope note in the beginning that this lesson is focused only on circulation data. This also begs the question if there will be other python intro lessons for other types of library data such as MARC21 or analyzing data for eResources (counter/sushi).

I would clarify who the intended audience is for this lesson. The emphasis is clearly cicrculation data. Though other learners can learn python basics. However, other library data doesn't seem within the scope of this lesson. Data such as xml, json, marc or working with APIs isn't addressed. In many ways, I wish this lesson was one of a series for python for libraries where here the focus is circulation, then another one on xml, another on marc, json, and apis.

I would also reword "It uses examples that are relevant to a range of library use cases." in the lesson description. I think this lesson addresses circulation use cases the best while other use cases in particular for data in other formats (xml, json, marc, html) and APIs aren't covered.

Which part of the content does your suggestion apply to?

Setup, Learner Profiles, and Episode 12: Data Visualisation