Learning Mathematics for Data Science is not an option.

As a Data Scientist, you must be familiar with important mathematical concepts and be able to think like mathematicians do.

If you are looking to accelerate your career in data science by taking a short course to upgrade your skills, these courses from the notable educators will edify you with the core skills that you need for Data Science.

**I know the options out there**; prerequisites and the skills you need to become a Data Scientist. So, Please refer to the **Closing Notes section** at the tail end of this piece, where I usually section related/ adjunctive resources.

## Top 13 Courses to Learn Mathematics for Data Science

I’ve compiled these math courses according to the difficulty level and also student rating data points.

**So**, without further ado, let’s get started.

### — Game Theory

This high-rated course is designed by Stanford University and The University of British Colombia and delivered via Coursera.

This course aims to solidify learners understanding about game theory and the mathematical modeling of strategic interaction among the rational and irrational agents.

#### Is it right for you?

This beginner-level course is suitable for anyone and if you have the basic knowledge of maths then you’ll surely get ahead faster.

By the end, you’ll have a good understanding of Game Theory, Backward Induction, Bayesian Game, and Problem Solving.

### — Mathematics for Data Science

This four-course specialization is designed by HSE to help learners become skilled in using wide range of mathematical tools required for Data Science and Machine Learning.

Through the series of guided lectures and hands-on exercises, you will dig a little deeper into real-world examples and problems arising in Data Science and learn to solve them in Python.

#### Is it right for you?

This specialization is suitable for beginners with basic knowledge in Python.

By the end, you will have gained solid understanding of Discrete Mathematics, Calculus, Linear Algebra and Probability for Data Science.

### — Introduction to Discrete Mathematics for Computer Science

This five-course interactive specialization is designed by the University of California San Diego and HSE to equip learners in discrete mathematics related to software engineering, data science, security and financial analysis.

Through the guided series of lectures and interactive puzzles, you will learn about the combinatorics, graphs, probability, number theory that are universally required.

#### Is it right for you?

This beginner level specialization is very suitable for learners who aspire to do the research level.

By the end, you will have gained knowledge in Graph Theory, Number Theory, Cryptography and Probability.

### — Data Science Math Skills

This course is designed by Duke University to equip learners with basic math knowledge required to be successful in advanced data science courses.

This course is excellent for learners who have basic math skills but may not have taken algebra or pre-calculus.

#### Is it right for you?

If you want to learn the math that data science is built upon, then this beginner level course will edify you with no extra complexity.

By the end you will gain familiarity with the unfamiliar ideas and gain skills in Bayes’ Theorem, Bayesian Probability, Probability and Probability Theory.

### — Linear Algebra for Data Science in R

This course is created by Eric Eager, who is a Data Scientist at Pro Football Focus and delivered via DataCamp.

In this course, you’ll learn to work with vectors and matrices, and solve matrix-vector equations, understand perform eigenvalue/eigenvector analyses, and use principal component analysis.

#### Is it right for you?

This course is suitable for learners with background in R programming and knowledge of high-school level mathematics.

By the end, you will have an advanced familiarity of Linear algebra used in data science.

### — Introduction to Mathematical Thinking

This brisk course is designed by Stanford University to equip leaners with the cognitive skills to think the way professional mathematicians do.

This course will edify you with the life-long skills to solve all the real-world, science and even mathematical problems.

#### Is it right for you?

This intermediate level course assumes background in maths, at least of high school-level.

By the end, you will have gained advanced familiarity in Number Theory and Real Analysis.

### — Introduction to Calculus

This course designed by The University of Sydney, emphasises on the key ideas of precalculus and the historical motivation for calculus.

This course is excellent for learning to develop and practice methods of differential calculus with applications and integral calculus.

#### Is it right for you?

This intermediate-level course is perfect to learn the threshold concepts in foundational mathematics

By the end, you will have advanced familiarity with the applications of mathematics in engineering, science and business.

### — Introduction to Logic

This is another high-rated course from Stanford University that aims to provide a thorough introduction to Logic from a computational perspective

You will learn to encode information in the form of logical sentences and understand logic technology and its applications in the filed of mathematics, science, engineering, and more.

#### Is it right for you?

This course is suitable for intermediate level learners who have a basic understanding of computational mathematics.

By the end, you be equipped with the skills in Relational Algebra, Problem Solving, Propositional Calculus and Mathematical Logic.

### — Matrix Algebra for Engineers

This course is offered by The Hong Kong University of Science and Technology and delivered via Coursera.

This course aims to equip learner with sufficient mathematical maturity and problem solving skills.

#### Is it right for you?

This course is suitable for intermediate-level learners who want to understand the basics of matrix algebra.

By the end, you will have gained solid understanding of Matrices, Systems of Linear Equations, Vector Spaces, and Eigenvalues and eigenvectors.

### — Calculus: Single Variable ( 5-Parts )

This course series is designed by The University of Pennsylvania and delivered via Coursera.

Through series of lectures, you’ll dig much deeper to understand Single Variable Calculus and Functions, Differentiations, Integration, and Applications.

#### Is it right for you?

This course is suitable for learners with high-school level knowledge of Mathematics.

After the successful completion of this course, you’ll become highly prepared to take advanced mathematics and data science courses.

### — Matrix Methods

This course is designed by The University of Minnesota and delivered via Coursera.

In this course, you will learn the basics of Matrix Methods, matrix-matrix multiplication, solving linear equations, orthogonality, and more.

#### Is it right for you?

This course assumes basic knowledge of high-school level maths and Python programming.

By the end, you will become highly prepared for learning advanced data science courses.

### — Differential Equations for Engineers

This course is designed by The Hong Kong University of Science and Technology, taught by Professor Jeffrey R. Chasnov and delivered via Coursera.

This course is a good place to learn about differential equations, both basic theory and applications.

#### Is it right for you?

This course is suitable to intermediate-learners and learners aspiring to work as Data Scientists.

By the end, you will have an advanced familiarity with Ordinary Differential Equation, Partial Differential Equation (PDE), and Engineering Mathematics.

### — Game Theory II: Advanced Applications

This course designed by The University of British Columbia and Stanford University is excellently taught by Matthew O. Jackson, Kevin Leyton-Brown, and Yoav Shoham.

You will learn the design interactions between agents and understand social choice theory, mechanism design, and auctions.

#### Is it right for you?

This advanced-course is suitable for learners with college-level knowledge in mathematics.

By the end of this course, you will have become highly prepared for working on data science projects.

**Closing Notes **

Learning Mathematics for Data Science is very challenging but not impossible.

I hope your journeys will go as you hope, and that resources from above and the following Educators will equip you with the data science core skills ( SQL, Pandas, Statistics and more ) you desire to build.

###### Thanks for making it to the end 😉

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**Image Credits** : Pinterest, DataCamp, Coursera, georgiasouthern.edu