If you think you’re awful at math to the point where you notice yourself tuning out any time you hear someone discussing numbers, you may be thinking that any kind of math-centric path like data analytics is far out of your reach. I’m here to tell you, though, that you can absolutely overcome your weak or long-forgotten math skills and become a data analyst.
Data analytics requires a lot of skills that aren’t just math, many of which you may already possess. And, with the right strategy, tools, and mindset, you can succeed at picking up the algebra and statistics you need to get started in the field. Let’s get into what you need to know in order to succeed.
How much math do you actually need to know?
What you need to learn to get started isn’t actually that scary; mostly, you need a good grasp of basic algebra, probability, and statistics. You’ll eventually need more if you’re going to get into machine learning/deep learning/AI, but focus on building the foundation first and you’ll be surprised how much you’re able to do even with that. The vast majority of reporting and analysis that you need to do involves the basics and not fancy algorithms.
To do data analysis, you also don’t need to be an absolute master of calculating all things by hand. I wouldn’t suggest shortcutting that part while you’re learning since it is helpful to go over it in order to better understand foundational concepts, but ultimately, you’ll be using Python or Excel to do most of the heavy lifting.
A lot of the coding you’re learning as you go along will also reinforce what you learn about statistics and teach it to you in a new way. The beauty of studying data analytics is that you re-encounter many of the same concepts over and over again in different ways, which really helps you retain what you learned. You’ll learn certain stats concepts, and then you’ll encounter them when analyzing dataframes in Python or doing formulas in Excel. As an added bonus, any stats concepts that may have seemed difficult or abstract are a lot clearer when you’re actually using them in Python and doing something with them.
If being a data analyst isn’t all math, then what is it?
A good part of data analytics involves learning these things that are technically not math:
So, you see that math and statistics knowledge is only a part of the broader skillset you need to be a data analyst.
Now, let's talk about how to learn the math you do need to know.
Learning Math: Mindset is key
If you think you’re bad at math, there might be a lot of things leading you to that conclusion. Maybe you had bad experiences in math classes in the past, labeled yourself as “not good at math,” and started living that narrative. There might also be external forces reinforcing that narrative as well. For example, others might look at you and make assumptions about what you’re good or bad at based on your career field and/or degrees. We tend to pigeonhole ourselves into either STEM or non-STEM fields, and that pigeonhole can become a prison. But why let disciplinary boundaries and past history define your potential?
Here’s the thing: No one is inherently bad at math. Math skills are not inborn, and they can be learned like anything else. (Someone said this to me when I was just starting out on my learning journey, and it was a serious “a-ha!” moment.)
Having the right mindset is so important if you are a non-math person hoping to enter the field, though, because you have to believe you can do it. If you already think you aren’t up to the task, you’ll fail before you even get started. Also, know that “math anxiety” is a real thing–there’s a fair amount of research on it (just Google). Becoming aware that you might have dealt with or are dealing with math anxiety will help you have a bit more compassion toward yourself and also give you insight into what you need to do in order to overcome this barrier and build your confidence.
In my case, I came from a humanities field. When I decided to get into data analytics, I hadn’t done statistics in years. What worked for me mindset-wise was focusing on curiosity, which I find works really well to centralize the learning process and get you out of the realm of self-doubt. This approach is actually one I used to use when I taught English to students who sometimes thought they were terrible at writing. If you can fire up enough curiosity and get truly curious about something, it changes the energy. You become open, interested, engaged, and even excited, and you stop focusing on whatever fears are holding you back.
So I got curious, and then I got going. I didn’t know how it was going to turn out. But I was surprised to find that not only could I do math, but I could understand it, and it was fun. It was like opening up a whole world I’d forgotten existed, and it has changed my point of view dramatically--and that is invaluable. You may discover the same.
Learning Math: Bring the right strategy to the table
To be a data analyst you do need some math skills, and I don’t want to minimize the steepness of the learning curve you might have depending on your current ability level. But I will say this: Never underestimate the power of determination and sheer force of will. Make a plan of attack for yourself, and then do not allow yourself to give up. Promise yourself that no day passes without studying, and live up to the promise.Over a period of several months, the learning really adds up.
Fortunately, there are so many math learning resources available online. I personally relied heavily on Khan Academy and then supplemented my learning with Youtube videos. You might also want to find a “math mentor” to whom you can ask questions if there are certain concepts you don’t understand… but if you do have specific questions, 99% of the time you can find that someone else asked a similar question online.
As a part of your learning strategy, it is also worth investing a bit of time into learning about the science of learning so you can get more efficient at learning something that may be out of your comfort zone. Two books by Barbara Oakley are worth picking up: A Mind For Numbers: How to Excel at Math and Science and Mindshift: Break Through Obstacles to Learning and Discover Your Hidden Potential. The former specifically addresses career changers, so if that’s you, you might find that one especially helpful.
What all of this means for you
The main takeaway here is this: Don’t let your fear of math–something you may have picked up very early on in life–hold you back from pursuing a career as a data analyst. Being a data analyst requires a lot of skills, only one of which is math. And, you’re more than capable of learning the math that you need for the job (and then some!) When you discover you enjoy it and are even good at it, the confidence you will gain from becoming good at something you once thought you couldn’t do is enormous.
