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GitHub for Data Scientists

44mIntermediate2020-12-01

Authors

Sara Anstey

Data Analytics Consultant

Madecraft

Full-Service Learning Content Company

Course details

Version control is rapidly becoming an essential skill for data scientists. In this course, learn how to get the most out of GitHub, not just as a code repository, but also as a resource for finding software and connecting with an engaged community. Review foundational GitHub concepts, from how GitHub actually works, to key terminology, to how GitHub facilitates collaboration for data science projects. Learn how to effectively use repositories in GitHub, including how to create and clone a repository and resolve common merge issues. Plus, learn how to create a strong data science portfolio with GitHub, contribute to open-source repositories, and more.

Skills covered

Version ControlData Science FoundationsGitHubSoftware Development ToolsPersonaData ScienceSoftware Development

Concepts

0. Introduction

01 - Collaboration is the key to data science

1. GitHub Foundations

02 - What is GitHub
03 - How does GitHub work
04 - Common GitHub terminology
05 - Using GitHub for collaboration
06 - Accessing learning resources

2. Using Repositories in GitHub

07 - Creating a repository in GitHub
08 - Cloning a repository in GitHub
09 - Branching in GitHub
10 - Commit in GitHub
11 - Pull requests in GitHub
12 - Resolving common merge issues

3. Creating a GitHub Portfolio

13 - The value of a GitHub portfolio
14 - Creating a powerful data science portfolio

4. Community Building

15 - Finding collaborators to follow
16 - Contributing to open source repositories

5. Common GitHub Resources

17 - Where to find answers
18 - Incorporating user interfaces

Conclusion

19 - Moving beyond the basics of GitHub