Charles Ndungu, a new data science learner, shares his first experience with Git and GitHub. He outlines a simple workflow: `init` → `add` → `commit` → `push` → `pull`. This basic sequence is enough for beginners to start tracking work and sharing it with others.

For data professionals, Git provides essential reproducibility, allowing you to revert to any project version. It enables safe collaboration where teams can work without overwriting each other's changes. Using version control is a standard expectation in modern data teams, providing a safety net for experimentation.

The guide details using Git Bash on Windows, covering installation, user configuration, and creating a first local project. Key commands include `git init`, `git add`, and `git commit`. The tutorial then walks through creating a GitHub repository, setting up authentication with a Personal Access Token (PAT) or SSH keys, and pushing code.

Ndungu highlights a real beginner mistake: a malformed remote URL with an extra dot caused a 404 error. He advises checking `git remote -v` to verify the exact path. The experience underscores that mastering these fundamentals is critical for anyone entering data science or engineering fields.