Applicants for our data science program often ask us whether they need a background in math to be successful at data science. It’s a fair question, but the answer isn’t exactly black and white. If you aced AP Calculus in high school or paid attention during college Stats, you’ll certainly have a leg up as a student in any data science bootcamp or online course. An understanding of mathematics theory will help give you the context needed for this highly analytical field — and if you like math, chances are good you’ll like the job, too.
That said, a background in math is definitely not a requirement to pursue a career in data science. With enough drive and curiosity, you can master the fundamental concepts needed to thrive in the role, even if you don’t consider yourself a “numbers person.”
So, how much math do you need in data science? Let’s break it down.
Coding and data analysis are both highly logical, methodical fields of study. If you skew right-brained, the mindset and approach required for data science might not feel natural at first — but that doesn’t mean you can’t overcome those challenges. It’s true, though, that you might have to work a little harder to put the pieces together to comprehend a more data-oriented framework.
If you’re considering a career in data science, you’ll need a baseline understanding of the principles and concepts in a variety of mathematical fields. Here are just a few areas of math that will be critical to your work:
This basic branch of math is fundamental to many areas of data science, particularly in understanding and building prediction-based models and machine-learning algorithms. You'll need to know how to graph a function on the cartesian plane (this is the basic algebra you learned in high school. For example, y=mx+b). You should also know how to multiply two matrices. Data is stored in matrices and manipulated to inform everything from how languages are automatically translated online to which Instagram ads you see.
Multivariable calculus works side-by-side with linear algebra to train algorithms to improve over time and deliver on their objective. Just a few of the calculus concepts you may need to be familiar with include derivatives and integrals, though understanding a bit about about gradients, maxima and minima, the chain rule, and beta and gamma functions will be helpful as well. Keep in mind that you don’t need to be a calculus whiz. You just need to be able to understand the core concepts well enough to apply them to your work.
Statistics & Modeling
Statistics is hands-down the most essential field of math for data science. You’ll want to have a strong foundation in the concepts of normal distribution, mean, and standard deviation to thrive in the Data Science program at Lambda School. Being competent and confident in your statistics skills will open doors for you in a variety of data science jobs, as you’ll use these principles daily in your role.
According to the LinkedIn 2020 Emerging Jobs Report, data scientists in today’s workforce are “augmenting responsibilities traditionally done by statisticians as some industries, like insurance, gear up for the future.” This trend is just one example of the link between math and data science, and a sure sign that we’ll continue to see increasing job opportunities as more industries rely on technology-driven data analysis. Getting a firm grasp on the mathematical concepts above (among others) will no doubt give you an advantage as you begin your career in data science.
So, you’re ready to become a data scientist, but you’re not particularly confident in your math skills. You’re not alone. For those with a less analytical background, the systematic, meticulous nature of data science might be a new frame of reference. But hey, you’re adaptable — right? Understanding math for data science is completely within reach.
If you aren’t quite ready to dive into a data science bootcamp or online course, there are a number of excellent (and free!) resources that can help you get started on the right path. In fact, even if you intend to pursue a more formal data scientist online training program, brushing up on your core math capabilities in preparation can’t hurt. Here are a few courses you might check out to kickstart your learning, especially if you’re a little rusty on those differential equations and improper integrals:
If the idea of functions, variables, and derivatives makes your palms sweat — or if all of this sounds a little too daunting — you might consider web development as an alternative career path in tech. It’s a job that comes with all the same opportunity and growth, with substantially less mathematics theory.
But if you’re feeling energized, excited, and ready to dive into math for data science, then it’s time to take action. According to the U.S. Bureau of Labor Statistics, the number of statisticians is projected to grow 33.8% from 2016 to 2026, the fastest of any occupation in mathematics. These roles are largely driven by the data science field, with massive job growth expected across numerous industries. Simply put, it’s a great time to be getting into data science.
Whether you’re a left-brain numbers person or have very little background in mathematics, you can probably benefit from a data scientist online training program. The Lambda School data science course was created in collaboration with industry experts and hiring managers to prepare students to successfully launch a career in tech. We’re not a data science bootcamp or university degree program — we offer a unique path to a rewarding career, with live online classes and a hands-on, collaborative learning environment. Our curriculum delves deep into the hard skills you’ll need as a data scientist, from linear algebra and statistics to Python and SQL.
Learn more about our data science program, or check out Lambda School alumni testimonials to see where you could be this time next year.