Learning Git as a Data Analyst

In data analytics, technical skills often focus on SQL, Python, or visualization tools. But one skill quietly powers collaboration in nearly every tech project: Git. Today, I took a deep dive into Git — the version control system that lets you track, share, and manage changes in your code or analysis projects. I wanted to learn the basics well enough to confidently push my work to GitHub, collaborate with others, and maintain clean project history.

Ina Reyes

8/17/20251 min read

Step 1: Understanding What Git Is

Git is like a time machine for your code.
It keeps snapshots of your project so you can:

Revisit earlier versions
See who made which change
Work on features in isolation before merging into the main project

Step 2: Setting Up Git

After installing Git, I configured my username and email so commits show up under my name on GitHub:

Step 3: Creating My First Repository

I created a folder, initialized Git, added a README, and made my first commit:

Step 4: Connecting to GitHub

I created an empty GitHub repo and linked it:

Now my code lives both locally and online.

Step 5: Branching and Merging

Branches are a way to work on features without affecting the main project.
I created a branch, made a change, and merged it back into main:

Step 6: My Git Cheat Sheet

Here’s the quick reference I created for myself (and now for you):

Step 7: What I Learned

Git helps keep work organized and recoverable
Branching is safer than editing main directly
GitHub is more than storage — it’s a collaboration hub

Next Steps

Use Git for every project, even small ones
Learn more about resolving merge conflicts
Explore GitHub Issues & Pull Requests for collaboration