4 Free Math Courses to do and Level up your Data Science Skills
Just as there is no Data Science without data, there's no science in data without mathematics. Strengthening your foundational skills in math will level you up as a data scientist that will enable you to perform with greater expertise.
When I got into Data Science and Machine Learning, anything related to math and statistics was something I had visited for the last time around 10 years ago, and I reckon that’s probably why I found it so hard at first. It took me lots of hours of reading and watching videos to get some ground understanding of how things happen for a lot of the tools we daily use in the industry. However, it got to a point where I felt the need for developing a solid understanding of what was happening underneath all those “imports” and “fits” I was doing on my Jupyter Notebook. So I decided it was time to wipe the dust off my math knowledge.
Nowadays, I’m still doing it, and I reckon it will never be enough. Moreover, coming from business and being in an industry full of professionals from engineering, statistics, physics, and other exact sciences, I know there are LOTS of things to learn in the world of Data Science. But you know what? Technologies and languages might come and go, but the mathematical background of the field is going to remain.
That’s why today I’m wrapping up a list of 4 courses to level up your math knowledge and take advantage of some of all this spare time we have been given thanks to this unfortunate situation. Since, you know, you should be staying at home these days.
1. Mathematics for Machine Learning
Where: Coursera
Involved institution: Imperial College London
Time required: 104 hrs (realistically it will be at least +50%)
Prior requirements: None
Abstract from the course:
For a lot of higherlevel courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics — stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.
Topics covered:
 Linear Algebra
 Multivariate Calculus
 Principal Component Analysis
TIP: most of Coursera’s courses and specializations have the option to audit them. You won’t get a certificate, but you’ll access most of the resources of the course—something I personally found more than enough. At the moment of enrolling, just select the option to audit the course.
2. Essential Math for Machine Learning: Python Edition
Where: edX
Involved institution: Microsoft
Time required: 50 hrs
Prior requirements: Python and some ground understanding of math.
Abstract from the course:
Want to study machine learning or artificial intelligence, but worried that your math skills may not be up to it? Do words like “algebra’ and “calculus” fill you with dread? Has it been so long since you studied math at school that you’ve forgotten much of what you learned in the first place?
You’re not alone. Machine learning and AI are built on mathematical principles like Calculus, Linear Algebra, Probability, Statistics, and Optimization, and many wouldbe AI practitioners find this daunting. This course is not designed to make you a mathematician. Rather, it aims to help you learn some essential foundational concepts and the notation used to express them. The course provides a handson approach to working with data and applying the techniques you’ve learned.
Topics covered:
 Equations, Functions, and Graphs
 Differentiation and Optimization
 Vectors and Matrices
 Statistics and Probability
TIP: this course has starting dates, but you can select a prior starting date and see all the content from that cohort for free.
3. Probability and Statistics in Data Science using Python
Where: edX
Involved institution: UC San Diego
Time required: 100–120 hrs
Prior requirements: multivariate calculus and linear algebra
Abstract from the course:
Reasoning about uncertainty is inherent in the analysis of noisy data. Probability and Statistics provide the mathematical foundation for such reasoning.
In this course, you will learn the foundations of probability and statistics. You will learn both the mathematical theory, and get a handson experience of applying this theory to actual data using Jupyter notebooks.
Topics covered:
 The mathematical foundations for machine learning
 Statistics literacy: understand the meaning of statements such as “at a 99% confidence level.”
TIP: this course has starting dates, but you can select a prior starting date and see all the content from that cohort for free.
4. Bayesian Statistics: From Concept to Data Analysis
Where: Coursera
Involved institution: Santa Cruz, University of California
Time required: 22 hrs (realistically, no less than 30 hrs)
Prior requirements: some ground understanding of probability.
Abstract from the course:
This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data. We will compare the Bayesian approach to the more commonlytaught Frequentist approach, and see some of the benefits of the Bayesian approach.
Topics covered:
 Probability and Bayes Theorem
 Statistical inference
 Priors and Models for Discrete Data
 Models for Continuous Data
I recommend doing these courses in the order presented, but of course, go ahead with any you like if you match the requirements.
Original. Reposted with permission.
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