Recorded February 21, 2018
The field of data science has seen enormous growth over the last few years. Organizations increasingly leverage data as a strategic asset that data scientists turn into meaningful insights.
Data science and machine learning are iterative processes for testing new ideas. Git and GitHub are ideal tools for tracking changes and collaborating within your own team and across the organization.
You will learn how to:
- Use GitHub repositories to organize your work
- Provide a clear and well-documented path for analysis
- Maintain quality by conducting code reviews and running automated tests
- Collaborate with peers (both inside and outside of your team)
- Host your rendered R or Jupyter notebooks directly from your GitHub repositories
- Allow others to validate and verify your findings, or learn from your experiences
Join us for this informative look at how GitHub can help streamline your data science workflows.
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Solutions Engineer, GitHub