# 15 Mathematics MOOCs for Data Science

The essential mathematics necessary for Data Science can be acquired with these 15 MOOCs, with a strong emphasis on applied algebra & statistics.

**By Matthew Mayo.**

Most of the mathematics required for Data Science lie within the realms of statistics and algebra, which explains the disproportionate number of these courses listed below. A few other areas are included to round out the list, including calculus, finite mathematics, and a few more advanced offerings; however, the essence of the skills on parade here are statistical and algebraic in nature.

Statistics, in particular, is at the very foundation of Data Science, and is the collection of tools which helps us separate significance from randomness. Algebra is quite often at the heart of the analysis itself. The further quantitative skills facilitate intuition, which is essential in analytics.

Whether you lack formal quantitative education, or are simply looking to brush up on these skills, the following MOOCs can help you achieve your goals.

**Getting in the Mood**

*These courses can help lay the foundation for quantitative thinking.*

**1. Introduction to Mathematical Thinking**

*School*: Stanford

*Platform*: Coursera

*Instructor*: Keith Devlin

*Dates*: Sep. 21 - Nov. 27

*Description excerpt*: [A] key feature of mathematical thinking is thinking outside-the-box - a valuable ability in today's world. This course helps to develop that crucial way of thinking.

**2. Math is Everywhere: Applications of Finite Math**

*School*: Davidson College

*Platform*: Udemy

*Instructor*: Tim Chartier

*Dates*: Self-paced (any time)

*Description excerpt*: Computer fonts, Angry Birds, March Madness, and Google - sound like fun? Indeed, finite math is engaging and influences the world around us.

**3. Model Thinking**

*School*: University of Michigan

*Platform*: Coursera

*Instructor*: Scott E. Page

*Dates*: Oct. 5 - Dec. 13

*Description excerpt*: Models improve our abilities to make accurate forecasts. They help us make better decisions and adopt more effective strategies. They even can improve our ability to design institutions and procedures.

**4. Introduction to Logic**

*School*: Stanford

*Platform*: Coursera

*Instructor*: Michael Genesereth

*Dates*: Sep. 28 - Nov. 21

*Description excerpt*: This course is a basic introduction to Logic. It shows how to formalize information in form of logical sentences. It shows how to reason systematically with this information to produce all logical conclusions and only logical conclusions. And it examines logic technology and its applications - in mathematics, science, engineering, business, law, and so forth.

**Algebra**

*These courses progress from introductory to real-world applications of algebra.*

**5. Introduction to Algebra**

*School*: School Yourself

*Platform*: edX

*Instructor*: Zach Wissner-Gross, et al.

*Dates*: Self-paced (any time)

*Description excerpt*: Algebra is an essential tool for all of high school and college-level math, science, and engineering. So if you're starting out in one of these fields and you haven't yet mastered algebra, then this is the course for you!

**6. Linear Algebra - Foundations to Frontiers**

*School*: University of Texas at Austin

*Platform*: edX

*Instructor*: Maggie Myers & Robert A. van de Geijn

*Dates*: Archived material (any time)

*Description excerpt*: Through short videos, exercises, visualizations, and programming assignments, you will study Vector and Matrix Operations, Linear Transformations, Solving Systems of Equations, Vector Spaces, Linear Least-Squares, and Eigenvalues and Eigenvectors.

**7. Applications of Linear Algebra Part 1**

*School*: Davidson College

*Platform*: edX

*Instructor*: Tim Chartier, et al.

*Dates*: Archived material (any time)

*Description excerpt*: This course is part 1 of a 2-part course. In this part, we'll learn basics of matrix algebra with an emphasis on application. This class has a focus on computer graphics while also containing examples in data mining. We'll learn to make an image transparent, fade from one image to another, and rotate a 3D wireframe model. We'll also mine data; for example, we will find similar movies that one might enjoy seeing.