added first draft gds intro (#430)

* added first draft gds intro

* ran rules, created assets, addressed comments

* added tip frame

* changed folder name

Author: henrardo

asciidoc/courses/gds-fundamentals/badge.svg

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+= Get started with the Graph Data Science library
+:usecase: recommendations
+:categories: data-scientist:1, data-analysis:10, intermediate:3
+:duration: 3-4 hours
+:caption: Learn the fundamentals of Neo4j Graph Data Science
+:status: draft
+:key-points: Graph projections, Algorithm execution, Algorithm configuration, Relationship aggregation, Projection modeling
+:graph-analytics-plugin: true
+
+== Course Description
+
+This foundational course takes you from GDS basics through to confident, independent algorithm work.
+
+You'll learn the fundamentals: what graph data science is, how to create projections for different analytical questions, how to choose and configure algorithms, and how to interpret results.
+
+By the end of this course, you'll be able to read GDS documentation independently, run any algorithm, and design projections that match your analytical goals.
+
+This course prepares you for the link:https://graphacademy.neo4j.com/courses/gds-applied-algorithms/[Applied Algorithms in GDS^] course, where you'll see these techniques solve real-world industry problems.
+
+The course automatically creates a new `movie recommendations` sandbox within link:https://sandbox.neo4j.com/?usecase=recommendations[Neo4j Sandbox] that you will use throughout the course.
+
+
+== Prerequisites
+
+This course is intended for analysts and data scientists who have:
+
+* Basic understanding of graphs and graph databases
+* Familiarity with basic Cypher query language
+* No prior GDS experience required
+
+
+== Duration
+
+3-4 hours
+
+
+== What you will learn
+
+* What graph data science is and how it differs from traditional data science
+* How to create and manage graph projections (monopartite, bipartite, multipartite)
+* The five main algorithm categories and when to use each
+* How to run algorithms in five different execution modes
+* How to configure algorithms with orientation, weights, and parameters
+* How to aggregate relationships during projection
+* How to design projections based on analytical questions
+* How to read GDS documentation independently
+
+
+[.includes]
+== This course includes
+
+* [lessons]#28 lessons# across three core modules
+* [challenges]#5 hands-on challenges# to test your understanding
+* [quizes]#Validation exercises# integrated throughout
+* Clear connections to the link:https://graphacademy.neo4j.com/courses/gds-applied-algorithms/[Applied Algorithms in GDS^] course

asciidoc/courses/gds-fundamentals/banner.png

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+= GDS orientation
+:type: lesson
+:order: 1
+
+Graph data science can refer to both the discipline 'graph data science', and 'Graph Data Science' or 'GDS', the Neo4j plugin. By the end of this lesson, you will understand:
+
+* The difference between GDS the product, and 'graph data science' the discipline
+* The difference between GDS the product and Aura Graph Analytics
+
+
+== The discipline of graph data science
+
+Graph data science, the discipline, applies data science methods to graph data. Graph-specific methods support analyses and insights that would be unfeasible or impossible to achieve with tabular data. 
+
+For example, traditional data science may detect fraud by flagging individual accounts and monitoring known suspicious activities. Such approaches do not consider coordinated networks as single components.
+
+Graph data science allows you to analyze entire networks of coordinated fraud that would be invisible to traditional methods. The image below demonstrates such an activity in action.
+
+image::images/lesson_1_fraud_detection_network.png[A connected component showing a transaction network]
+
+The network shown in the image represents a single connected component of transaction data within a graph of financial transactions. By isolating connected components in this way, the graph data analyst can reveal networks of fraudulent behavior that would otherwise remain inaccessible.
+
+== Graph Data Science (GDS)
+https://neo4j.com/docs/graph-data-science/current/[Graph Data Science], or GDS, is a Neo4j plugin. 
+
+You can use GDS with https://neo4j.com/docs/aura/classic/aurads/[Neo4j’s AuraDS cloud] and self managed Neo4j installations. It is also available as a plugin for Neo4j Desktop.
+
+GDS allows you to run the algorithms, build the pipelines and create the predictive models used by graph data scientists. It is optimized for speed and ease-of-use.
+
+Throughout this course, we will use 'Graph Data Science' or 'GDS' to refer to the Neo4j product and 'graph data science' when referring to the discipline.
+
+Later, you will learn how to access and use GDS. 
+
+== Aura Graph Analytics
+https://neo4j.com/docs/aura/graph-analytics/[Aura Graph Analytics] is an on-demand service that allows you to use the Graph Data Science library regardless of where your source data is stored. 
+
+It runs in Aura as a dedicated service optimized for analytics workloads, with no memory or compute resources shared with your data store. While functionally similar to GDS, Aura Graph Analytics is a different product.
+
+While this course may reference Aura Graph Analytics from time to time for context, it will always use GDS for demonstration.
+
+However, even if you intend to use Aura Graph Analytics, this course will still provide you with the prerequisites necessary for any subsequent Aura Graph Analytics course.
+
+
+[.quiz]
+== Check your understanding
+
+include::questions/1-graph-database-vs-tabular.adoc[leveloffset=+1]
+
+include::questions/2-gds-vs-aura-analytics.adoc[leveloffset=+1]
+
+
+[.summary]
+== Summary
+
+You've learned the distinction between graph data science as a discipline and GDS as a Neo4j product. Graph data science applies specialized methods to reveal insights impossible with traditional data science—like detecting coordinated fraud networks.
+
+GDS is a Neo4j plugin available across multiple platforms, while Aura Graph Analytics is a dedicated cloud service. Both enable graph data science workflows.
+
+In the next lesson, you'll see these concepts in action as you explore what makes graph data science different from traditional approaches.

asciidoc/courses/gds-fundamentals/course.adoc

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+[.question]
+= Comparing graphs and tabular data
+
+How do graph databases differ from tabular databases?
+
+* Graph databases are more popular than tabular databases
+* Tabular databases are as efficient as graph databases at traversing relationships
+* [x] Graph databases store relationships between entities
+* Tabular databases are easier to use than graph databases
+
+[TIP,role=hint]
+.Hint
+====
+Graph databases store relationships and entities.
+====
+
+[TIP,role=solution]
+.Solution
+====
+Graph databases store entities and relationships with the same level of importance.
+====

asciidoc/courses/gds-fundamentals/illustration.svg

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+[.question]
+= GDS and Aura Analytics
+
+What is the key difference between GDS and Aura?
+
+* [x] Aura Graph Analytics is a dedicated service optimized for analytics workloads
+* GDS can only be used in cloud deployments
+* Aura Graph Analytics is required to use the Graph Data Science library
+* GDS and Aura Graph Analytics are the same product with different names
+
+[TIP,role=hint]
+.Hint
+====
+Think about where each product runs and how compute resources are managed.
+====
+
+[TIP,role=solution]
+.Solution
+====
+Aura Graph Analytics runs as a dedicated service optimized for analytics workloads with no memory or compute resources shared with your data store. GDS is a plugin that can be used with AuraDS, self-managed Neo4j installations, and Neo4j Desktop.
+====
+