Math for Data Science in One Picture: What do you REALLY need to study?

In my last blog post, I covered the statistics you need to know for data science. But of course, stats isn’t the only math related knowledge you need. Rather than offer my own biased opinion about the importance of this subject vs. that one, I performed a meta analysis of popular opinion to see what data scientists and educators are saying (see the reference list below).

The following picture shows each math area’s relative importance to data science (based on how many times it was recommended). The larger the bubble, the more people recommended that particular subject.

Bear in mind the above list isn’t exhaustive; It’s just what was mentioned most frequently by the article authors. I’ve only included topics that got more than a couple of mentions. So, just because a field isn’t mentioned, it doesn’t mean that it isn’t important. I also tried to lean towards pure “math” rather than math that’s data science oriented (like bootstrapping).

If you want to know more about the topics, start here (these correspond to the largest bubbles):

Descriptive Statistics: Charts and Graphs

Probability theory: Bayes’ Theorem

Hypothesis Testing

Calculus: Functions