Mikhail Klassen

"GitHub is one of the largest social coding platforms on the web. Its collaborative features allow GitHub users to follow each other's code developments, build off each other's work, and make it easier than ever to create open source software. Based on content from the book "Mining the Social Web" (O'Reilly Media) by Matthew Russell, this course shows you how to mine GitHub data for insight into the platform's projects and community of users. For example, you'll be able to trace open source project histories, the types of programming languages used in those projects, and the relative popularity of those languages. The course teaches you how to make API requests on GitHub's developer platform, use NetworkX to construct interest graphs from GitHub data, and create visualizations of graphs. To get the most out of the course, learners should have basic Python experience and a GitHub account."--Resource description page.

Using Python and NetworkX to Gain Insight Into GitHub's Usage and Community of Users

Mining the Social Web, GitHub

Matthew A. Russell

Mine the rich data tucked away in popular social websites such as Twitter, Facebook, LinkedIn, and Instagram. With the third edition of this popular guide, data scientists, analysts, and programmers will learn how to glean insights from social media—including who’s connecting with whom, what they’re talking about, and where they’re located—using Python code examples, Jupyter notebooks, or Docker containers. In part one, each standalone chapter focuses on one aspect of the social landscape, including each of the major social sites, as well as web pages, blogs and feeds, mailboxes, GitHub, and a newly added chapter covering Instagram. Part two provides a cookbook with two dozen bite-size recipes for solving particular issues with Twitter. Get a straightforward synopsis of the social web landscape Use Docker to easily run each chapter’s example code, packaged as a Jupyter notebook Adapt and contribute to the code’s open source GitHub repository Learn how to employ best-in-class Python 3 tools to slice and dice the data you collect Apply advanced mining techniques such as TFIDF, cosine similarity, collocation analysis, clique detection, and image recognition Build beautiful data visualizations with Python and JavaScript toolkits

Mikhail Klassen

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