You Don’t Have to Be a Math Wizard to Work in Data Analytics

Sarah Rajani
May 8
6 min read

Updated: May 9

Tearing down the myth that you need to be a math genius in order to get into data analytics

Woman on laptop looking over her shoulder confused. — What do I need to know?

When I was a kid, I genuinely thought I was bad at math. I was never a "math person." It wasn't that I didn't understand how to do the calculations or solve equations... those things I could do, and I actually enjoyed those things. It was the word problems that I dreaded and struggled with. You know the ones. Where you get asked an obscure question and have to figure out how to solve the problem. The setups were confusing, the wording was strange. I didn’t even know where to begin:

If two trains leave different cities at different times, traveling at different speeds… What kind of ham sandwich did they have on board?

What?!

A guy at his laptop looking panicked with his hands on his head — The look of pure confusion.

Okay, I overexaggerated a tiny bit there, but you see what I mean.

I would reread the question over and over and still not know where to start. It wasn’t the math, per se, that confused me. It was how the problem was written. The way the details were jumbled together, you had to untangle the story before you could even got to the calculations.

And that's why, growing up, I figured I just wasn’t a math person. I was a writer and artist and was interested in the workings of the brain. Not math.

It never felt like something I was good at. And I definitely never thought it would be part of my career.

So how did I end up in a field where I use numbers daily? It's something I wondered about myself, but reflecting on it these past few months, I think it makes sense now.

I Didn't Take a Straight Path Into Data

In high school, I started learning calculus, and I found myself really enjoying it. Growing up, I had always loved algebra, so it made sense that I would take a liking to calculus. Maybe it was the logic and structure that I was drawn to. There were no riddles or trick questions. Just clear steps and logical patterns.

I even ended up getting a second bachelor's degree in math and statistics. My courses were heavy in calculus, linear algebra, and all kinds of stats, and I really enjoyed it. I especially took a liking to stats, where I could use calculations to determine probabilities and detect levels of significance.

But even then, I didn’t walk straight into a math or data career. Data wasn't as popular when I was in school, and my goal was to get into med school, anyways. But after all that schooling, I was burned out and tired and just wanted to enter the workforce.

So after graduating, I found myself working in foreign exchange, investigating wire transfers and handling complex transactions. It was a completely different world than I was used to and wasn't something that particularly appealed to me at the time. But I spent years building a career in that space.

Then data started becoming more popular, and it wasn't until a few years later that I started getting pulled into data projects. I began working more with reports, trends, and transaction patterns. Little by little, I found myself drawn to the problem solving side of it — the investigation part — where I was able to use my experience to figure out where a wire transaction went wrong, why, and how to resolve it.

The part that surprised me was that I was good at it. Not because it was easy, but because it wasn’t like those word problems from middle school. The problems I solved in my job had real context. They made sense. I wasn’t decoding riddles or trying to guess what a question was asking. I was investigating real issues, finding patterns, and using logic to work through them.

That was when everything started to come together. Almost like it was meant to be. The logical thinking, the curiosity about how things work...

It happened slowly. One project led to another, and somewhere along the way, I realized, I actually like working with data and numbers.

Data Analytics Isn’t What I Thought It Would Be

When you hear “data analytics,” it sounds intimidating. You imagine deep math, complex statistics, and coding everywhere. But most data analytics roles don’t look like that, especially when you are starting out.

A lot of the work involves:

Asking the right questions.
Cleaning up messy data.
Reviewing and updating other people's reports.
Helping people understand what the data is telling them.

We're not solving complex equations but, of course, some math skills help (especially if you’re working with percentages, averages, trends, or some basic stats). You definitely don’t need to be a mathematician. You also don’t need to have loved high school math. You don’t even need to have enjoyed calculus (though I would love to use calculus in my data work!).

You just need to be willing to learn, practice, and get comfortable working with data, and taking it one step at a time.

You Don’t Need to Be a Math Wizard to Get Into Data Analytics

If you're thinking about getting into data analytics but worried because you never felt like a "math person," here’s what helped me:

Focus on Building Your Logic and Problem-Solving Skills

You don't have to memorize formulas. Focus on thinking through problems clearly and making sure you understand what is really being asked (this is key!). And practice explaining what the data means, not just calculating it.

Start with the Right Tools

Learn SQL, Excel, and a BI tool of your choosing (like Power BI, Tableau, or Looker), as these tools will help you analyze data (and no advanced math is involved!). Play with sample datasets to get comfortable, by building projects and working on practice problems.

Be Okay with Learning as You Go

No one expects you to know everything on day one. No one knows everything, and you won't be expected to either. You will look things up. Google will become your friend. It’s completely normal. The goal is to understand the basics well and be able to build on your knowledge as you go.

You need to realize that much of analytics is about cleaning, organizing, and interpreting, not advanced math. If you can spot when something looks off, you’re already thinking like an analyst.

You Belong in Data, Even If You Never Saw Yourself Here

If you were never the top of your math class, or if you’ve thought of yourself as “not a math person," it doesn’t mean you can’t work in data. And it certainly doesn’t mean you can’t be really good at it.

I used to think I wasn’t a math person because I disliked with the way problems were written, with their vague setups, confusing questions, and no clear place to start. But in analytics, the problems make sense. They’re grounded in real situations; they have real business context. Instead of guessing what the question is asking, you’re working through it with logic, patterns, and actual data.

That’s why I stuck with it. And why I think more people could too.

Working in data is about helping people make better decisions. And a lot of the time, it’s about making messy things make sense, not solving abstract train problems from a textbook.

You’re allowed to grow into it. You’re allowed to start where you are. I did, and you can too.

That same girl who used to panic over obscure word problems now spends her days solving actual business problems with data. And she's pretty good at it. Who would have thought?

Did you dislike math growing up, too?

Do you wonder if data analytics is the right field for you?

Let me know in the comments!