A Beginner’s Guide

When I graduated from Oral Roberts University in 2013 with a degree in Mathematics and Psychology, I thought, I will probably find my psychology degree useful, but what will I ever do with math? Ha, to my parent’s delight, as well as my own, I find myself now in a field rich in mathematical applications and possibilities! Don’t worry, you don’t need to get a whole degree in math to be a good data scientist, but having some sort of mathematical foundation is the cornerstone to a success in this ever-evolving field. Mathematics is a huge area of study, but I’m here to guide you on what you should learn or, if you’re like me and already have a background, what areas you should brush up on to get started in data science.

“Mathematics provides the necessary tools and techniques to make sense of complex data sets“

First, how relevant is math to data science? Well, data science, at its core, revolves around harnessing the power of data to extract valuable insights and make informed decisions. It involves working with massive datasets, analyzing patterns, developing predictive models, and uncovering hidden relationships. That’s great, but what makes all of this possible? You guessed it—mathematics.

Mathematics provides the necessary tools and techniques to make sense of complex data sets. It allows data scientists to apply statistical analysis, build machine learning models, optimize algorithms, and draw meaningful conclusions from the vast amounts of information at their disposal. As I delved deeper into the world of data science, I realized that my mathematical knowledge was not only valuable but also indispensable.

So, you get it, math is important! Now, which mathematical topics should you focus on as you are getting started?

1. Learn Statistics. One of the key areas where mathematics plays a vital role in data science is statistics. Understanding statistical concepts such as probability distributions, hypothesis testing, and regression analysis allows data scientists to interpret data accurately and make robust predictions. By applying mathematical principles, we can identify trends, measure the uncertainty of our findings, and validate the significance of our results.
2. Learn Linear Algebra. Another significant branch of mathematics in data science is linear algebra. Linear algebra provides a framework for working with multi-dimensional data, such as matrices and vectors. It enables us to perform operations like matrix multiplication, eigendecomposition, and singular value decomposition. These operations are crucial for tasks like dimensionality reduction, feature extraction, and clustering—techniques that help us uncover hidden patterns and gain deeper insights from complex datasets.
3. Learn Calculus (at least through calc 2, but I’ve used calc 3 as well). Calculus, too, plays a prominent role in data science. Differential calculus enables us to understand the rates of change, identify optimization points, and optimize machine learning models. In machine learning, gradient descent utilizes partial derivatives of multiple variables to locate local and global minima. Integral calculus helps us analyze cumulative effects, calculate areas under curves, and tackle problems related to probability distributions. By utilizing calculus, data scientists can fine-tune models, minimize errors, and optimize decision-making processes.

“embrace mathematics as your ally and invest time in strengthening your mathematical foundations”

It’s been 10 years since I graduated, but returning to my mathematical roots has been exciting. Mathematics forms the bedrock of data science, allowing us to unravel the hidden insights within data, build accurate models, and make informed decisions. So, if you’re a newbie in the field of data science, embrace mathematics as your ally and invest time in strengthening your mathematical foundations. You’ll soon discover the true magic and potential that mathematics holds within the realm of data science.