Math and Data Analysis are practically synonymous terms when you work in the tech industry. It’s why many people get scared away from working in analytics because there’s this preconceived notion that math is all the job entails.
In reality, while I can’t say that there isn’t some math involved, I can tell you that the math you do is actually quite fun. There’s non of the theoretical or imaginary numbers scariness that comes with being a data scientist – instead, you look at practical, easily applicable mathematics to explain what happened and to even make simple predictions about what could happen.
You might even already know all of the math you’ll ever need to become a data analyst. Heck, I put together a 15-minute read on everything you would learn in an undergraduate-level statistics course to get you started. It’s that simple.
As someone who once failed a math course in high school, I can share with you with absolute certainty that this data analysis learning roadmap for people who hate math is exactly all you need – nothing more and nothing less – to help you take your first steps in the data analysis field. All you need is to be open-minded about learning just a little bit of math and the rest will be easy. You will probably spend more time learning to code and how to conduct data analyses than you will be learning all of the math you will need for the job.
This roadmap looks at all of the learning aspects you will need to cover to become a data analyst, with just a bare-bones plan for the bare minimum level of mathematics you need to succeed in the job.
Step 1: Learn Excel, SQL, and Python
Author’s note: In previous roadmaps that I’ve created, I’ve always suggested that people looking to become data analysts should learn Python or R. I’ve changed my approach to this simply due to the difficulties that come with learning R (and because Python is widely the industry standard). If you’re learning Python as your first and one-and-only Programming language, you have enough functionality at your fingertips to have a long and happy career as a data analyst.
Python is the easiest programming language you can pick up when first jumping into data analysis. It’s pretty powerful, has tons of functionality, and also has many applications outside of data analysis if you decide the field isn’t for you. It’s incredibly forgiving, gives you exactly what you ask for (almost literally, so it makes you good at asking the right questions), and allows you to produce effective analyses without having to become an expert in all of the minutiae that comes with programming.
My top tip for learning Python is to work on practice problems from Kaggle or Leetcode. As much as this kind of repetition may seem boring, it’s the quickest way to test yourself and force your brain to become accustomed to the patterns of programming problems.
But first, before you even begin to think about learning Python, you need to learn Excel. Why? Because Excel might be the only tool you need to become a data analyst.
Excel (while old-fashioned to some) is still an incredibly powerful tool that you can use to conduct most simple data analyses that involve summaries of company data as well as the establishment and simple prediction of trends. We all say that we will "actually" learn Excel one of these days – this is your sign to actually learn the ins and outs of Excel because you may find your first entry-level position at a small business as a data analyst because of this skill alone.
One of my favorite ways to learn the hundreds of tricks built into Excel is to look for TikTok videos of people sharing Excel hacks. There are hundreds of short, bite-sized videos that can help you quickly learn all of the many shortcuts built into the program which can speed up your workflow and help get you more accustomed to the program through this form of microlearning.
Finally, you will learn SQL, a language that allows you to work with data in a database. SQL can be a complex language to master, which is why one of my best tips is to create a cheat sheet for yourself as you go along. This cheat sheet should show you everything from the proper syntax for SQL commands to the different types of data joins you can create.
You don’t have to memorize every function, but you should become familiar with ones like COUNT, CONCAT, TRIM, MAX/MIN, GETDATE, and CONVERT. Memorizing functions is a waste of your time. As I mentioned previously, make a cheat sheet highlighting how to use some of your most-used functions and learn how to Google the right questions to find the other functions instead.
One of the best ways that I became familiar with SQL quickly was to download a free database and then go through it trying out all the functions and syntax along the way. Having a database that you can muck around in without being concerned about making mistakes or wrecking the database is a great way to get your hands dirty with the code (the best way to learn SQL in my opinion).
Step 2: Refresh your memory of algebra and statistics
If you can do algebra and statistics, you’ve basically got everything you need to be a data analyst.
In fact, if you go through my article that outlines an undergraduate statistics course in fifteen minutes, you’re pretty much already through the worst of it. See? I said this was a data analyst learning roadmap for people who hate math.
An Undergraduate-Level Introductory Statistics Course in 15 Minutes
The truth is that you can always learn more math when you’re a data analyst. You can never know enough math. However, if you truly despise it, you can get away with knowing algebra and statistics.
To get a grasp of the basics, I would recommend going through Khan Academy’s lecture series for Algebra 1, Algebra 2, and AP Statistics.
Algebra 1 | Math | Khan Academy
If you would like a challenge, Khan Academy’s Linear Algebra series is a fantastic way to expand your skills, as is Professor Leonard’s lecture series on Calculus 1 on Youtube.
All of these resources share mathematical knowledge in pretty painless ways, which allows you to zip through the learning math part of becoming a data analyst and getting to the good stuff: data analysis and visualization.
Step 3: Study data analysis and visualization
It’s time to tie it all together and analyze some data. This involves learning how to ask the right questions, collect the data you will need, clean the data, analyze the data for insights and answers to your questions, and interpret the data through a visualization that can be easily understood by anyone.
To better study data analysis and visualization, I believe in spending less time working through online courses and more time doing the actual thing. For example, the video I linked above is 4 hours long. This is all the time you should be taking in learning how to do a data analysis before creating an analysis of your own. You will learn more about data analysis when working on your own project than sitting in front of your computer following along with a video.
From experience, this is the only video you need to watch to learn how to analyze data – there isn’t much more to learn beyond this. You can expand your knowledge by learning how to use a particular visualization tool or you can learn about how to make your statistics more accurate, but these are areas of professional development – not core learning.
Final thoughts
See? That wasn’t so painful (read: full of math).
In fact, math plays probably the smallest role in the learning process, especially if you already know some of it. The best part of becoming a data analyst is that you don’t really need to know how the math works (like if you were a data scientist) – you just need to know that it works and when to use it.
When I was self-teaching myself data analysis, I found that I spent the least amount of time learning math. Most of it was spent learning how to code and working on data analysis projects to fine-tune my skills. So, don’t let your hatred of math steer you away from what is essentially one tiny aspect of the entire job. When you realize that the math you’re using is extremely practical, you learn to enjoy using it to uncover details and answers to your questions. As long as you’re comfortable working with some numbers in a practical sense, like a challenge, and can tell a good story using data, you can become a data analyst with no issues.
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