# Data Science 101: Getting a Free Mathematics Education for Data Science

In this special feature, Daniel D. Gutierrez, Managing Editor of insideBIGDATA provides a number of free educational resources designed for budding data scientists to gain a foundation in mathematics. In addition to being a big data journalist, Daniel is also a practicing data scientist, educator and sits on a number of advisory boards for various start-up companies.

An ever increasing number of people are scrambling to enter the field of data science these days and many such candidates find themselves weak in the mathematical foundations of computer science, mathematics, statistics and probability theory – all necessary disciplines for any data scientist (not to be confused with a big data engineer who doesn’t necessarily need this kind of background). All newbie data scientists should strive to take on “the bible” of data science Elements of Statistical Learning and fully understand this math-heavy resource. The elephant in the room is how can someone who desires to enter the field, quickly get up to speed with the math?

As a mathematician and data scientist myself, I took some time to find some free online resources that anyone can use to gain this mathematical background. I’ve divided up the list of courses into the following categories: Prerequisites (as needed), Calculus, Linear Algebra, Differential Equations, Probability and Statistics, and electives. In some cases, I’ve included more than one option for a specific topic area. You can preview the courses, in such cases, and decide which one is better for you. Best practices would be to select a couple of courses from each category. The academic level for all courses is undergrad.

Once you get this material under your belt, you’ll be fully grounded in your pursuit of becoming a data scientist.

Prerequisites

Intermediate Algebra (UC Irvine via Coursera)

Pre-Calculus (UC Irvine via Coursera)

Calculus

Single Variable Calculus (MIT OpenCourseWare)

Calculus One (Ohio State University via Coursera)

Calculus 1B: Integration (MIT via edX)

Calculus 1C: Coordinate Systems and Infinite Series (MIT via edX)

Calculus: Single Variable (University of Pennsylvania via Coursera)

Multivariate Calculus (Ohio State University via Coursera)

Calculus Two: Sequences and Series (Ohio State University via Coursera)

Linear Algebra

Linear Algebra (MIT OpenCourseWare)

Introduction to Linear Models and Matrix Algebra (Harvard University via edX)

Linear Algebra – Foundations to Frontiers (University of Texas at Austin via edX)

Differential Equations

Differential Equations (MIT OpenCourseWare)

Introduction to Differential Equations (Boston University via edX)

Linear Differential Equations (Boston University via edX)

Introduction to Partial Differential Equations (MIT OpenCourseWare)

Probability and Statistics

Introduction to Probability and Statistics (MIT OpenCourseWare)

Introduction to Statistics: Inference (UC Berkeley via edX)

Introduction to Statistics: Probability (UC Berkeley via edX)

Introduction to Statistics: Descriptive Statistics (UC Berkeley via edX)

Electives

Analytic Combinatorics (Princeton University via Coursera)