This course equips you with professional version control strategies essential for collaborative data science projects. Beginning with fundamental GitHub integration for Jupyter notebooks, you'll establish a solid foundation in tracking, sharing, and managing analytical code.

Collaborate and Code: GitHub with Python and Jupyter

Collaborate and Code: GitHub with Python and Jupyter

Instructor: Professionals from the Industry
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There are 2 modules in this course
This introductory module focuses on implementing professional version control practices for data science and programming notebooks. You'll learn how to integrate your analytical work with GitHub's powerful tracking and collaboration features, enabling you to maintain organized project histories and share your work effectively. Through video instruction, hands-on lab experience, and knowledge validation, you'll establish the technical foundation needed for professional code management while being introduced to the EngageMetrics project that will contextualize your learning.
What's included
1 video1 reading1 assignment1 ungraded lab
This advanced module explores cutting-edge techniques to enhance your data science version control workflow. You'll first discover how to leverage generative AI to create comprehensive, consistent documentation that improves project clarity and team collaboration. The module then progresses to sophisticated branching strategies specifically designed for data science experimentation, enabling you to manage multiple analytical approaches simultaneously without compromising code stability. Through videos, hands-on labs, and interactive dialogues, you'll develop a professional-grade version control workflow that combines AI efficiency with structured experimentation techniques.
What's included
2 videos2 readings1 assignment2 ungraded labs
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