Real data science work is iterative: you try an idea, change the code, break something, and want to get back to the version that worked. This module covers the two tools that make that safe and repeatable: Git and GitHub for tracking and sharing your code, and Jupyter notebooks for running experiments and explaining results in one place.
You'll start with the core Git workflow: staging, committing, and reviewing history. Then you'll learn branches, merging, and pull requests, which are how teams collaborate on shared code. Finally, you'll get comfortable with Jupyter, the notebook environment where most data exploration happens.
With your work tracked and your notebooks running, you're ready to pull in real data in SQL & Data Sources, the next module in this course.
A beginner-friendly start: common Git and GitHub terms, installing Git, creating and cloning a repository, and adding or removing files.
A deeper pass through the working directory, staging area, and repository, plus core commands, merge conflicts, stash, revert, rebase, and syncing with GitHub.
Creating a repository, working with branches, and making and merging pull requests, the workflow you'll use when collaborating with a team.
An introduction to the Jupyter project, covering Jupyter Notebooks and JupyterLab, the environments used for interactive data exploration.