Want to break into data science but don’t have a background in math? Maybe you’ve thought about data science as a career, but you’re worried about the math needed for data science because you think you’re not a numbers person.
First of all, can you actually break into data science without a background in math or STEM? The answer is yes! While data science requires a strong knowledge of math, the important data science math skills can be learned — even if you don’t think you’re math-minded or have struggled with math in the past.
In this sponsored post with Practicum by Yandex, we’ll break down how much math you need to know for a career in data science, how math is used in the field, and how to go about learning the mathematical concepts required for the role.
Practicum by Yandex is a fully online bootcamp for self-driven individuals designed by a top tech company. It offers comprehensive support from data professionals who can help fill in gaps in math knowledge.
If you’re at the early stages of considering a career in data science, you probably have a bunch of questions. Is data science hard? Do you need a degree to be a data scientist? And the most important question for you at a personal level: “should I become a data scientist?”
We’ll cover these and more below! Rest assured, it is possible to get into data science without a background in math. Here are some practical steps to take to get there. 👇
Table of Contents
- I Don’t Like Math. Is Data Science For Me?
- How Is Math Used in Data Science?
- How to Learn Math for Data Science
- Skills Needed to Break into Data Science
- Is Data Science Hard?
- How to Get Data Science Experience
- Examples of People Who Broke Into Data Science
- Should I Become a Data Scientist?
I Don’t Like Math. Is Data Science For Me?
If you don’t like math or struggle with statistics, data science can still be a great career for you — as long as you’re willing to take the time to learn some important mathematical concepts.
The first thing to know is that, as a data scientist, you will need to know a certain level of math for data science. As Practicum by Yandex puts it: “Mathematics forms the basis of all data science.” Data science math will almost always be part of your job, but certain industries will use it more heavily than others.
For example, data scientists working in academia often practice “theoretical” data science, which is much more math-focused. Industry professionals, on the other hand, often practice “practical” data science which is often much less mathematically intense.
Many times, you’ll just need to know how to use certain data science tools, without needing to know all of the math for data science behind those tools.
If you already know high-school-level math or are willing to invest time in learning key concepts, not being “good” or not liking math shouldn’t be a huge obstacle. Plus, remember that it’s never too late to become good at math and develop a passion for it.
Anton Eremin, Product Lead of data science programs at Practicum, says: “Don’t dismiss a data science career just because you don’t have stellar math credentials. You need a solid understanding of a relatively modest amount of advanced mathematics to be good at most junior data science positions. You can master that amount with a couple of months’ worth of theory and then extensive practice that we provide in Practicum throughout the program.”
However, if you really dislike math and don’t want to ever look at numbers or equations, you may want to reconsider pursuing this field. In the end, there’s no getting around the math needed for data science!
Eremin adds, “Don’t think your math journey will be over by graduation: most professionals continue learning on the job through most of their career and you’ll need to do the same.”
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How Is Math Used in Data Science?
Math for data science is used to help identify patterns in data, create and apply algorithms, make calculations, build predictive models, preprocess data, understand data on a higher level, and much more.
So now that you know that math is an important part of data science, how much math is actually used in data science?
Depending on the role, you will likely need to know the following data science math skills:
- Statistics and probability: Very important in data science for making estimates and predictions from data. Also essential for correctly applying algorithms. This is the type of math that is arguably the most important for data science, and in most cases, you’ll need to know this type of math most intimately.
- Linear algebra: A mathematical discipline that involves vectors, matrices, and transformations. Many data science models are implemented using linear algebra.
- Calculus: Sounds scary, but you don’t need to know everything about calculus for data science. Understanding the key principles is often enough. Calculus is used in data science to calculate derivatives, minimize sums of squares, create algorithms, etc.
(Don’t worry if this all sounds like gibberish, we’ll be explaining how to learn these important concepts later in this post!)
How to Learn Math for Data Science
How can you learn the relevant math needed for data science? Which order should you learn data science math skills in?
The first piece of advice is not to learn and study only the statistics concepts before doing any practical data science work. This bottom-up approach can make you feel discouraged and can be math overload.
⬇️ Instead, try a top-down learning approach. Try learning to code first, understanding key data science concepts, trying out fun projects, then the math element will make more sense in context. As this Stanford article explains, “Professor Jo Boaler says students learn math best when they work on problems they enjoy, rather than exercises and drills they fear.”
When it comes to learning math for data science, you can start by taking individual courses in topics like linear algebra, applied statistics, probability theory, and calculus. Consider online courses or attending a class at your local community college if you prefer in-person learning.
The great thing is that learning the fundamentals won’t take too long. You can learn a lot of key principles in under an hour.
🎧 Check out this podcast episode on How to Teach Yourself Data Science With David Venturi or this one on Learning Data Science as a Beginner.
Practicum by Yandex is also a great option for learning the full gamut of data science skills. For those who are worried about learning math, the Practicum team is creating a separate course to prepare students before they make a final decision to begin a career in data science.
Practicum’s data science program includes courses on statistics and linear algebra, designed for those without a statistics background. Their programs also emphasize the immediate application of what students learn, so you won’t be learning math in a vacuum — they teach you what you’ll be using, rather than just focusing on complex theory.
Eremin says: “We built the Practicum data science program with people with a math-scarce background in mind. Learning data science and required fundamental math is two jobs in one, so it’s never easy. However, we find that people have an easier time getting their heads around math concepts when you can explore their applications right away in real-world examples. And so this is how it’s done in Practicum: we introduce you to necessary mathematical concepts and formulas, and then you master their application through relevant real-world cases.”
Skills Needed to Break into Data Science
So, we’ve established that mathematics is definitely important for data science, but what else do you need to know to get into data science? Foundational data science skills include:
- Programming languages: The main data science coding languages to know include Python or R, and SQL
- Data visualization: Displaying data in graphs, charts, etc.
- Machine learning: Building smart machines and algorithms to help process data and learn as they go. Check out these 13 machine learning courses, and learn relevant libraries such as Scikit-Learn, XGBoost, PyTorch, and LightGBM
- Exploratory data analysis: Performing initial scans to detect patterns in data
- Data preprocessing: How to collect and clean data, handling missing and duplicate values, changing data types, etc.
There are also soft skills to consider, including an appetite for learning (since the industry is constantly evolving!), communication and being a team player, etc. To get an awesome job, you want to be a person people want to work with!
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Is Data Science Hard?
How hard is data science? Well, it’s important to start by saying that very few things worth doing are easy. Data science is no exception: you’ll need determination and a voracious appetite for learning to make it into the field.
However, it can be very helpful to have a structured program like Practicum to guide you through the things you need to know. You won’t be set adrift to cobble together courses yourself: you’ll just need to focus your efforts on succeeding at the curriculum designed to prepare you!
🎓 This also leads us into the question: do you need a degree (or Ph.D.) to be a data scientist? As you may have guessed by now, you certainly don’t. College is just one option of places to learn data science, but it’s not the only one!
How to Get Data Science Experience
How do you get relevant experience in data science without a degree? Start by committing to learning and completing personal data science projects to build experience (and your portfolio!). Here’s how to come up with ideas for projects.
In Practicum by Yandex’s data science program, you’ll build a portfolio of 15 projects, including helping a mobile company analyze user behavior and analyzing potential profit and risks for an oil company.
Once you’ve practiced your skills on personal projects, try branching out by helping local businesses in your area with data science or data analysis projects. For example, can you help a local restaurant adjust its staffing and stock to increase savings and reduce waste?
You can also try contributing to open source data science projects.
Try taking side hustles or even a full-time job in data-science-adjacent roles to learn extra skills on the job. For example, you could get a job or freelance gig as a data analyst, product analyst, growth marketing analyst, etc.
Examples of People Who Broke Into Data Science Without a Math Background
Let’s get introduced to some Practicum graduates who show that you don’t need to be a lifelong math whiz to discover a passion for data science!
With a background as a mechanic, Jaylen was looking to make a career change—and his eyes drifted toward the tech world. When he brought it up, people he knew discouraged the idea, saying he didn’t have a math degree or background and it might be too difficult for him.
But instead of letting them get inside his head, Jaylen’s goal was to prove to himself that he could.
“Before attending Practicum by Yandex, I did not come from a technical background nor a math background. I had been looking at bootcamps for some time and came across Practicum. After researching each of the different curriculums I decided that data science is the one I was most interested in. Shortly after I started the full program I discovered a passion for data science I didn’t know that I had. The coursework was beyond expectations. They didn’t assume you had knowledge of all the math behind algorithms and they didn’t use terminology that only professional mathematicians would know.”
His best advice? Don’t be scared to try something new. Jump right in and do it!
The son of migrant farmworkers, Danny grew up in a household without a TV or a computer. He was born in San Quintín, a pueblo in Baja California Sur, Mexico. Then, when he was seven, his parents brought him and his younger brother to California, where life began to change. Still, education was never a focus for him until high school, when he discovered a passion for psychology during a summer course.
Fast-forward to college, when Danny enrolled in UC Riverside’s psychology program. He got an internship with the Partnership for Public Service, which involved transcribing interviews with experts on the ethical implications of AI and its likely impact on the workplace.
AI genuinely intrigued him, and he knew that data science was a highly-paid and promising field. So when his thoughts kept drifting back to the internship a year later, Danny started studying Python, then signed up for Practicum’s Data Analyst program.
He’s now working toward a master’s degree in Applied Data Science.
Should I Become a Data Scientist?
If you’re craving a career change and your mind keeps drifting back to data science, you owe it to yourself to give it a try. Maybe it’ll just cement that you really don’t want to tackle any math you can’t do on your smartphone calculator — and that’s okay! But if it awakens a new passion for you, then do yourself a favor and lean into that.
Take Eremin’s advice: “Set your mind to it, take the first step, land your first job and never stop learning. After a couple of years, you’ll be surprised to see how much headstart you’ve gained on your colleagues with a math degree.”