When I started my journey to become an AI Engineer, I knew I had to get comfortable with not just coding, but also understanding data, analyzing it, and communicating results.

So, I picked something fun and familiar: Netflix’s global catalogue. It sounded simple at first: grab the dataset, analyze it, plot some graphs. But this project turned out to be a lot more than that - it became my first real encounter with data storytelling and version control (GitHub).

The Beginning

I downloaded the Netflix dataset (around 8,800 titles) and jumped straight into Jupyter Notebook. Rows of data: type, director, cast, country, release year, genre… At first glance, it was overwhelming.

But that’s the beauty of data — once you start exploring, stories begin to appear.

What I Found Inside Netflix’s Data

Some of the insights I uncovered:

- Netflix has far more Movies than TV Shows (no surprise, but the difference is huge).
- Content releases exploded after 2015, showing Netflix’s aggressive expansion.
- The US and India are major content contributors, with surprising variety from smaller countries too.
- A handful of genres dominate the catalogue, but there’s a healthy mix of niche content.
- Certain directors and actors keep appearing again and again.

And of course — I made some colorful visualizations to bring these points to life.

The Tools Behind the Scenes

I leaned on a stack of tools that I’ll definitely carry into future projects:

Python & Pandas → cleaning and wrangling the data
Matplotlib & Seaborn → turning numbers into visuals
Jupyter Notebook → my playground for experiments
Git & GitHub → where the real learning happened

My Biggest Challenge: GitHub

Here’s where things got interesting.

I thought the hardest part would be analyzing Netflix data. Nope. The hardest part was pushing my code to GitHub.

At first, I got errors like:

“fatal: pathspec did not match any files”
“Updates were rejected because the remote contains work that you do not have locally…”

It felt like speaking to Git in the wrong language. But slowly, I figured it out — learning how to:

Add and commit changes
Pull before pushing
Handle rebase conflicts
Finally see that sweet green checkmark when the push succeeded

Honestly, this was the most valuable part of the project - my first real step into version control.

What I Took Away

By the end of it, I didn’t just learn about Netflix’s content. I learned how to:

Ask questions of a dataset
Use Python to uncover insights
Communicate results with visualizations
Work with GitHub without panicking at error messages

It wasn’t perfect, but it was my first complete project - and that feels huge.

See the Project Yourself

You can check out the full project here on GitHub:
midknight247/netflix-content-explorer: Data analytics mini-project on Netflix dataset

What’s Next?

This project was about exploration. Next, I want to add machine learning into the mix - maybe predicting ratings, clustering genres, or building recommendation systems.

But for now, I’m just proud I pushed my first project to GitHub, wrote about it here, and hit "publish."

Here’s to the next one.

Netflix Content Explorer — My First Data Analytics Project