Detailing the Git workflow for data scientists

Understanding Git workflows is a key competency for data scientists. As we’ve discussed before, Git allows you to track changes, revert to previous versions, and collaborate with others. In this section, we’ll describe a typical Git workflow for a data scientist and explain the concept of a branch, an important feature in Git.

A branch in Git is essentially a unique set of code changes with a unique name. Each repository has one default branch (usually called master or main) and can have multiple other branches. The branches are used to develop features isolated from each other. When you want to create a new feature or experiment with something without disturbing the main line of development, you create a new branch. If the experiment is successful, you can merge these changes into the main branch. If it’s unsuccessful, you can discard the branch, and it won’t affect your main branch or repository.

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