How Six Years of Studying Mathematics Led Me Back to Its True Purpose

How Six Years of Studying Mathematics Led Me Back to Its True Purpose

Six years of studying mathematics and data science and yet, for the longest time, I felt like I was carrying knowledge with no destination.
A few months after finishing my Master’s in Data Science, I sat with a close friend, both of us math graduates talking about where life had taken us.
We laughed about how we used to spend nights proving theorems we didn’t understand and how we mastered tricks to pass exams rather than actually feeling the math. I said something that stuck with me:

“They made us prove theorems, but no one ever proved to us why math even mattered.”

It was a moment of clarity. We weren’t confused, we were never given context.

Six Years of Learning… For What?

I have a BSc in Mathematics and an MSc in Data Science. That’s six years. Hundreds of hours of lectures. Proofs. Sets. Limits. Integrals. Vector spaces.
Yet for a long time, I felt like I was carrying a heavy toolbox but didn’t know what I was building.
I knew math was “important,” but no one ever really showed us where it lives in the real world.
Until when I started my MSc in Data Science, where I Found Math Again, In Machines

As I studied machine learning, I had this “aha!” moment.
All these fancy models that everyone’s talking about classification models, regression models, facial recognition, recommendation engines e.t.c they weren’t just code. They were mathematics in motion.

• Neural networks? That’s just linear algebra and activation functions.

• Gradient descent? It’s calculus playing teacher, helping models learn.

• Probabilities? That’s how AI deals with uncertainty, just like we do.

• Loss functions and entropy? That’s information theory, alive and breathing.

I suddenly realized:

Machine learning isn’t replacing math. It’s math finally doing something loud enough for the world to notice.

So Why Do So Many Miss the Connection?

Here’s what I think:

We’re often taught math as a ladder, climb the rungs, pass the exams. But no one shows you the view from the top.
In tech and data science, people jump straight to tools:

• “Here’s how to build a model.”

• “Here’s how to use TensorFlow.”

That’s useful, but it’s like giving someone a spaceship without telling them the laws of physics behind flight.

Yes, you can fly it, but you won’t know why it crashes.

The Core Mathematical Ideas Behind AI

Let’s break it down simply. These are the hidden engines:

And this is just the start. You don’t have to be a math genius to appreciate this, you just need someone to walk you through it. 

The next time you use an AI tool, whether it’s a Spotify playlist or a face-unlocking phone, pause and think: there’s math behind that magic. From linear algebra to calculus, these concepts aren’t just abstract ideas; they’re the scaffolding of the future.

But one thing I do know is this:

Math is not a waste. You just need the right lens to see what it's building.