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It turns out that a lot of people — including engineers — are often times scared of mathematics.

The truth is, people who are good at math have lots of practice doing math. It takes time and effort to find/ create a sweet spot for maths, but it’s certainly not something you’re born with.

The greatest challenge in learning Mathematics is to remain focused so that you can avoid carelessly overlooking small errors and missing on daily opportunities for self-improvement.

It doesn’t require mindless repetitions but **deliberate practice** with focused attention to learn something difficult like Coding, Science, Maths and Music.

Before you start with any course, take a look how Myelinated Neurons in our brain perform before and after practice.

In this piece, I don’t assume basic comfortability with linear algebra/matrix calculus but if you do, you will definitely get ahead with the following courses.

If you need to an introduction to Probability and Statistics for Data Science, i’ve already got you covered 😀

My goal is to suggest the resources that will **equip and edify** you to master the vocabulary, notation, concepts, and algebra rules to be more successful in almost any Data Science and Machine Learning course.

# The 7 Best Mathematics Courses for Machine Learning and Data Science.

Below, I’ve curated a list of best online courses to learn Mathematics for Machine Learning and Data Science.

These classes will give you a sense of the math education and help you cultivate mathematical thinking, you’ll need to be effective in your Computational work, whatever that may be!

### — Mathematics for Computer Science

Mathematics for Computer Science is offered by University of London Goldsmith, designed to equip you with mathematical foundations needed to work in computer science.

If you dont have a basic knowledge of Mathematics, you’ll richly benefit for this course and become highly prepared to learn more advanced topics in Mathematics for Data Science and Machine Learning.

#### Is it right for you?

If you want to learn the basics of Mathematics in Computer Science, this is a perfect course to learn Numerical Mathematics and build skills to use computational tools.

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### — Math Behind Moneyball

Math behind Moneyball is a pretty unique course offered by University of Houston System.

In this course you will learn how regression, probability, and statistics can be used to help baseball, football and basketball teams improve performance.

With hands-on practice, you will also learn to analyze how decisions and betting is done using data.

#### Is it right for you?

If you don’t have a solid background in Mathematics and Statistics, this course will help you to gain foundational skills in Statistics, Regression Analytics, Microsoft Excel, Probability and more…

Upon the successful completion of this course, you will be highly prepared learn advanced Math topics for Data Science and Machine Learning.

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### — Data Science Math Skills

This course offered by Duke University and is designed to teach learners the basic math required to be successful in almost any data science or machine learning math course.

This course was developed for learners who may have basic math skills but don’t have knowledge of algebra or pre-calculus.

In this course, you will also learn about the core mathematics that data science is built upon, with no complexity.

#### Is it right for you?

If you require a general introduction to the math skills needed for data science, this course is suitable for learners to be fully prepared for success with the more advanced Mathematical concepts.

Upon the successful completion, you will be familiar with unfamiliar ideas in maths and will have learned all the important math symbols.

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# — Mathematics for Machine Learning – Linear Algebra

This course on Linear Algebra is offered by Imperial College London and you will start with looking at what linear algebra is and how it relates to vectors and matrices.

Then you will learn about vectors and matrices are and how to work with them, including the knotty problem of eigenvalues and eigenvectors, and how to use these to effectively solve problems.

Finally, you will work with datasets in a creative way — How to rotate images of faces, How to extract eigenvectors to look at how the Pagerank algorithm works.

#### Is it right for you?

This course is suitable for learners who are curious about how data-driven applications work and you will also learn to write code blocks with Jupiter notebooks in Python.

There are no prerequisites for taking this course and this course is also part of **Mathematics for Machine Learning Specialization** that i’ve added in this article as well.

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### — Linear Algebra for Data Science in R

Linear Algebra for Data Science in R is offered by DataCamp.

First you will receive an introduction to linear algebra and then you’ll learn how to work with vectors and matrices, solve matrix-vector equations.

Finally you will perform eigenvalue/eigenvector analyses and use principal component analysis to do dimension reduction on real-world datasets.

#### Is it right for you?

This course will use R to perform all analyses, one of the world’s most-popular programming languages.

If you want to learn R Programming, i’ve got you covered in this piece about R for Data Science..

If you have some experience in R programming, this course is perfect place to master the most important mathematical topics required in Data Science, and Machine Learning as well.

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### — Mathematics for Machine Learning Specialization

This Specialization is offered by Imperial College London to equip learners with the prerequisite mathematics for applications in data science and machine learning.

This specialization aims to getting you up to speed in the underlying mathematics, helping you build an intuitive understanding, and relating it to Machine Learning and Data Science.

#### Is it right for you?

If you are preparing for a higher level courses in Machine Learning and Data Science, this course will teach you everything you need to master the basics as well as advanced mathematics for Machine Learning and Data Science.

By the end of this specialization, you will have gained strong mathematical knowledge and skills for Eigenvalues and Eigenvectors, Principal Component Analysis (PCA), Multivariable Calculus and Linear Algebra.

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### — Introduction to Mathematical Thinking

Introduction to Mathematical Thinking is offered by Stanford, designed to help you learn to think the way professional mathematicians do.

This course will provide a fantastic introduction to the world of college-level math to help you inculcate numerical cognitive skills.

You will also learn to develop a crucial way of thinking, you’ll need to be successful in your Data Science and Machine Learning journey.

#### Is it right for you?

If you have a basic knowledge of Mathematics, this course will intellectually bootstrap your ability for numbers and forever change the way you approach problems.

By the end of this course, you will have a solid basis for Number Theory, Real Analysis, Mathematic Logic and Language.

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If you liked this article, I’ve got a practical reads for you. One about the Learning SQL for Data Science and one about **Best **Machine Learning Courses, on the Internet.

I’ve also got this Data-Centric Newsletter that you might be into. I send a tiny email once or twice every quarter with some useful resource I’ve found.

☟Don’t worry, I hate spam as much as you. Feel free to subscribe.

**Banner Image Credits**: wifflegif.com/gifs/620362-computing-math-gif

**Image Credits:** Coursera, DataCamp

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