Data science work is only useful if you can come back to it, explain it, and let someone else reproduce it. This module covers the everyday tools that make that possible: version control to track every change, notebooks to explore data interactively, and environments to keep projects from breaking each other.
You'll start with Git and GitHub: commits, branches, and sharing code. Then you'll move to Jupyter notebooks, where code, output, charts, and notes live together, and finish with virtual environments so each project has its own set of packages.
With a clean workflow in place, you'll be ready for the next module, SQL & Data Collection, where you'll start pulling real data from databases and APIs.
What Git is, how commits and branches work, and why version control is a basic requirement for any technical role.
Installing Git, making commits, working with branches, and pushing your projects to GitHub to share and collaborate.
Setting up Jupyter, running code cells, mixing in Markdown notes, and the shortcuts that make notebooks fast to work in.
Creating isolated environments, installing packages with pip, and keeping each project's dependencies separate and reproducible.