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
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