Git is a version control tool that helps you track changes in your code and files over time. GitHub is an online platform where you store Git repositories, share projects, and build a public portfolio. For data analysis projects, Git and GitHub are useful because they help you manage notebook versions, back up your work, and show completed projects to others.
Step 1 is installing Git. Download Git for your operating system and complete the installation. After installation, open a terminal (Command Prompt, PowerShell, or Terminal) and check that Git is working by running a basic version command. Then set your name and email in Git configuration so your commits are linked correctly.
Step 2 is creating a GitHub account and preparing your profile. Use a professional username and add a simple profile description. Your GitHub profile becomes a visible place where recruiters and teams can review your work.
Step 3 is setting up a local project folder. Create one main folder for this course and keep a clean structure inside it, such as data, notebooks, scripts, and outputs. Add a README file that explains what the project is about.
Step 4 is starting a Git repository. Initialise Git inside your project folder, then add a .gitignore file so you do not upload unnecessary files like large raw datasets, temporary files, or environment folders. After that, add your files and make your first commit with a clear message.
Step 5 is connecting to GitHub. Create a new repository on GitHub, then connect your local folder to it and push your code. After pushing, check your repository on GitHub to confirm files are visible.
Step 6 is maintaining good habits. Commit regularly with meaningful messages, push updates after finishing milestones, and keep your README updated. If you work on new features or changes, use branches to keep your work organised.
This setup ensures your projects are backed up, easy to track, and ready to share as part of your data analysis portfolio.
