Math in Data Science


Math is like an octopus: it has tentacles that can reach out and touch just about every subject. And while some subjects only get a light brush, others get wrapped up like a clam in the tentacles’ vice-like grip. Data science falls into the latter category. If you want to do data science, you’re going to have to deal with math. If you’ve completed a math degree or some other degree that provides an emphasis on quantitative skills, you’re probably wondering if everything you learned to get your degree was necessary. I know I did. And if you don’t have that background, you’re probably wondering: how much math is really needed to do data science? In this post, we’re going to explore what it means to do data science and talk about just how much math you need to know to get started. Let’s start with what “data science” actually means. You probably could ask a dozen people and get a dozen different answers! Here at Dataquest, we define data science as the discipline of using data and advanced statistics to make predictions. It’s a professional discipline that’s focused on creating understanding from sometimes-messy and disparate data (although precisely what a data scientist is tackling will vary by employer). Statistics is the only mathematical discipline we mentioned in that definition, but data science also regularly involves other fields within math. Learning statistics is a great start, but data science also uses algorithms to make predictions. These algorithms are called machine learning algorithms and there are literally hundreds of them. Covering how much math is needed for every type of algorithm in depth is not within the scope of this post, I will discuss how much math you need to know for each of the following commonly-used algorithms: