Data science work boils down to managing code and files, which raises key questions about storage, tracking changes, and collaboration. Early methods like shared folders and USB drives were clunky, leading to accidental overwrites and lost work. This inefficiency spurred the development of Version Control Systems (VCS) to automate tracking and enable safe teamwork.

Git stands out as the dominant distributed version control tool, created by Linus Torvalds for Linux kernel development. Its speed and open-source nature have made it the standard for over 93% of developers. The guide walks through installing Git for Windows, configuring a global user, and initializing a local repository with sample files, demonstrating core commands like `git init`, `git add`, and `git commit`.

For data scientists, mastering Git prevents collaboration disasters and creates a reliable audit trail for project evolution. Setting up a repository is the first step toward using platforms like GitHub. The next phase involves learning branching and merging to manage concurrent work streams without conflict, a critical skill for any modern technical team.