How Our Interns Relearned Math for Data Science – The Smarter Way

Main Author: Lalitha Srinivas, Co-Founder & Chief Architect, Peopleverse

Every year, we have the privilege of hosting driven and curious interns from top universities worldwide. What they share is not just academic merit, but a deeper hunger to understand how ideas come alive in real work settings.

Many arrive with one common worry:

“I’m not sure I’m strong in math—can I still succeed in data science?”

Over time, we’ve seen that the answer is a resounding yes—if you learn math the right way. Not through abstract theory or memorization, but by gradually building intuition and applying it in meaningful, contextual ways.

This is a reflection of what many of our interns discovered during their time with us: that the math behind data science is far more accessible—and more impactful—than it first appears.

Part 1: Statistics & Probability

Start here. Always.

Statistics is the backbone of every data insight. But more than formulas, what really matters is statistical thinking—the ability to ask, “What does this number really mean?”

What interns explored:

Descriptive statistics: More than just averages and medians—these measures became tools to observe how patterns emerge.

Probability: Understanding randomness, conditional logic, and belief-updating gave interns the ability to see uncertainty clearly.

Hypothesis testing: They learned to test ideas, not just assume them—using t-tests, chi-squares, and confidence intervals as instruments of rigor.

Part 2: Linear Algebra

Where data lives—in rows, columns, and spaces.

Most interns hadn’t worked with vectors or matrices outside the classroom. But when they began interpreting data in higher dimensions, the usefulness of linear algebra became vivid.

Key takeaways:

Vectors and matrices became methods for representing information more precisely.

Matrix multiplication and transformations helped them understand how data could be reshaped and manipulated.

Eigenvalues and eigenvectors revealed structure and direction—concepts that felt abstract earlier suddenly became intuitive.

What changed was not their background, but their lens.

Part 3: Calculus

Because learning means optimizing.

Many interns had only seen calculus as a set of academic exercises. But once they saw its role in optimization, they began to appreciate its elegance.

What they focused on:

Derivatives and gradients: Understanding how systems adjust and improve based on rate of change.

Gradient descent: A simple but powerful idea that helped them see how models learn by minimizing loss.

Practical thinking: We encouraged them to skip complicated integrals and focus on the calculus that drives real learning.

The outcome? A mindset that linked motion to meaning.

Part 4: Advanced Topics in Small Doses

When ready, go deeper—but stay grounded.

Not all interns explored this territory, but those who did went with purpose.

Information theory helped them see how to measure uncertainty.

Optimization theory introduced them to the idea of efficiency in decision-making.

Bayesian reasoning helped them look at data not as a static truth but as a system of evolving beliefs.

These concepts weren’t rushed. They were explored naturally, when a problem—or curiosity—demanded it.

Wrapping Up: What This Teaches Us All

Our interns taught us as much as they learned.

They reminded us that math is not a gate—it’s a bridge.

That concepts click not when you study them harder, but when you contextualize them better.

And that with the right guidance, anyone can develop the confidence to work meaningfully with data.

If you're starting out, here’s a thought:

Begin with clarity, not complexity.

Start small, learn by doing, and build trust in your own ability to reason.

And most importantly—don’t be afraid of math.

It’s not here to judge your intelligence. It’s here to sharpen it.