GitHub for Data Scientists
4.0 /5- Offered byLinkedin Learning
GitHub for Data Scientists at Linkedin Learning Overview
GitHub for Data Scientists
at Linkedin Learning
Duration | 1 hour |
Mode of learning | Online |
Difficulty level | Intermediate |
Credential | Certificate |
GitHub for Data Scientists at Linkedin Learning Highlights
GitHub for Data Scientists
at Linkedin Learning
- Earn a sharable certificate
GitHub for Data Scientists at Linkedin Learning Course details
GitHub for Data Scientists
at Linkedin Learning
Skills you will learn
More about this course
- 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
- Learn how to effectively use repositories in GitHub, including how to create and clone a repository and resolve common merge issues
- Learn how to create a strong data science portfolio with GitHub, contribute to open-source repositories, and more
GitHub for Data Scientists at Linkedin Learning Curriculum
GitHub for Data Scientists
at Linkedin Learning
Introduction
Collaboration is the key to data science
GitHub Foundations
What is GitHub?
How does GitHub work?
Common GitHub terminology
Using GitHub for collaboration
Accessing learning resources
Using Repositories in GitHub
Creating a repository in GitHub
Cloning a repository in GitHub
Branching in GitHub
Commit in GitHub
Pull requests in GitHub
Resolving common merge issues
Creating a GitHub Portfolio
The value of a GitHub portfolio
Creating a powerful data science portfolio
Community Building
Finding collaborators to follow
Contributing to open source repositories
Common GitHub Resources
Where to find answers
Incorporating user interfaces
Conclusion
Moving beyond the basics of GitHub
GitHub for Data Scientists at Linkedin Learning Faculty details
GitHub for Data Scientists
at Linkedin Learning
Sara Anstey
Director of Data Analytics and Integration at Novacoast
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GitHub for Data Scientists
at Linkedin Learning
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