This one picture shows **what areas of calculus and linear algebra** are most useful for data scientists.

If you read any article worth its salt on the topic *Math Needed for Data Science*, you’ll see calculus mentioned. Calculus (and it’s closely related counterpart, linear algebra) has some very narrow (but very useful) applications to data science. If you have a decent algebra background (which I’m assuming you do, if you’re a data scientist!) then you can learn all of the calculus you need in a few hours of study.

You don’t usually need to know exactly how to take derivatives, minimize sums of squares or create clustering algorithms from scratch–there are calculators for that! But if you have a general idea of what’s working in the background you’ll be able to recognize **when results don’t make sense** or what **better alternatives** might be available.

## References

MATH7502: Mathematics for Data Science 2 (Linear Algebra and Topics…

How Much Math Do You Need to Become a Data Scientist?

Cluster Analysis: Basic Concepts and Algorithms

The Mathematics Behind Principal Component Analysis

Lossy Compression

Fuzzy Relation Calculus in the Compression and Decompression of Fuz…

Credit: Data Science Central By: Stephanie Glen