GitHub for Data Scientists

GitHub for Data Scientists

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 0h 44m | 705 MB


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.

+ Table of Contents

1 Collaboration is the key to data science
2 What is GitHub
3 How does GitHub work
4 Common GitHub terminology
5 Using GitHub for collaboration
6 Accessing learning resources
7 Creating a repository in GitHub
8 Cloning a repository in GitHub
9 Branching in GitHub
10 Commit in GitHub
11 Pull requests in GitHub
12 Resolving common merge issues
13 The value of a GitHub portfolio
14 Creating a powerful data science portfolio
15 Finding collaborators to follow
16 Contributing to open source repositories
17 Where to find answers
18 Incorporating user interfaces
19 Moving beyond the basics of GitHub