100+ Math Online Courses from the World’s Top Universities — Class Central

In this article, I compiled 100+ online courses offered by the 60 best universities in the world for studying math in 2022.

I did so by combining popular university rankings to identify the best institutions, and then, using the Class Central database to find all their math online courses.

If you’d like to skip to the course list, click here. If you’d like to know how I built the list, or if you’d like to look at the raw data or Jupyter Notebook, keep reading.

Methodology

Top-10 combined world university ranking for studying math in 2022

I built the list following the same data-driven approach I used to build my list of computer science courses from the top CS universities.

First, I identified the leading world university rankings. Since I was specifically interested in math, I looked at their latest rankings of the best universities for studying math (or closest superset). Here are the rankings I ended up using:

Second, I scraped each ranking. In the simplest cases, this involved finding the underlying API, allowing me to directly request the ranking data in JSON format using Scrapy. In the most complex case, this involved crawling the ranking page by page using Playwright.

You can find the implementation details and raw data in my GitHub repo.

Third, I used JupyterLab to process the data. In essence, this involved cleaning the raw data, normalizing the university names across the rankings, and combining the three rankings into one by averaging the position of each institution across each ranking.

As you can see in the image above, I found that the top three math institutions are:

You can also find the data processing details and full ranking in my GitHub repo.

Fourth, I used the Class Central database, with its close to 60K online courses, to find all the math courses offered by the universities in the ranking.

The end result is a list of 100+ online courses offered by 60 best universities in the world for studying math in 2022.

Notable Courses

A handful of courses in the list are also part of Class Central’s Best Online Courses of All Time. You can find these notable courses below. They’re all excellent options.

• Matrix Algebra for Engineers from The Hong Kong University of Science and Technology ★★★★★(529)
• Fibonacci Numbers and the Golden Ratio from The Hong Kong University of Science and Technology ★★★★★(209)
• Introduction to Mathematical Thinking from Stanford University ★★★★☆(52)
• Probability – The Science of Uncertainty and Data from Massachusetts Institute of Technology ★★★★★(33)

Subjects

The full list is split into subjects. Click on a subject below to go to the relevant section. Courses also in Class Central’s Best Online Courses of All Time are denoted with a star (⭐).

With 100+ courses to pick from, I hope you find something you like. But if these aren’t enough, check out the Class Central catalog, which has close to 60K online courses, many of them free or free-to-audit.

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Best Mathematics Online Courses (25)

• Fibonacci Numbers and the Golden Ratio from The Hong Kong University of Science and Technology ★★★★★(209) ⭐
• Introduction to Mathematical Thinking from Stanford University ★★★★☆(52) ⭐
• How to Learn Math: For Students from Stanford University ★★★★☆(16)
• Effective Thinking Through Mathematics from The University of Texas at Austin ★★★★★(14)
• Convex Optimization from Stanford University ★★★★★(8)
• Mathematical Thinking in Computer Science from University of California, San Diego ★★★★★(2)
• A-level Mathematics for Year 12 – Course 1: Algebraic Methods, Graphs and Applied Mathematics Methods from Imperial College London ★★★★★(2)
• Fun with Prime Numbers: The Mysterious World of Mathematics from Kyoto University ★★★★☆(2)
• Introduction to Graph Theory from University of California, San Diego ★★★★☆(1)
• Cours préparatoire: Fonctions Trigonométriques, Logarithmiques et Exponentielles from École Polytechnique Fédérale de Lausanne ★★★★☆(1)
• Cómo Aprender Matemáticas – Para Estudiantes from Stanford University
• Semantics of First-Order Logic from Stanford University
• Graph Theory and Additive Combinatorics (Fall 2019) from Massachusetts Institute of Technology
• Transfer Functions and the Laplace Transform from Massachusetts Institute of Technology
• Delivery Problem from University of California, San Diego
• [New] Mathematics for Engineers: The Capstone Course from The Hong Kong University of Science and Technology
• A-level Further Mathematics for Year 12 – Course 1: Complex Numbers, Matrices, Roots of Polynomial Equations and Vectors from Imperial College London
• A-level Further Mathematics for Year 13 – Course 1: Differential Equations, Further Integration, Curve Sketching, Complex Numbers, the Vector Product and Further Matrices from Imperial College London
• [New] Further Mathematics Year 13 course 2: Applications of Differential Equations, Momentum, Work, Energy & Power, The Poisson Distribution, The Central Limit Theorem, Chi Squared Tests, Type I and II Errors from Imperial College London
• 离散数学 from Shanghai Jiao Tong University
• Nonlinear Dynamics and Chaos from Cornell University
• More Fun with Prime Numbers from Kyoto University
• Introduction to Complexity Science from Nanyang Technological University
• 离散数学概论 Discrete Mathematics Generality from Peking University
• Discrete Mathematics from Shanghai Jiao Tong University

Best Statistics & Probability Online Courses (37)

• Intro to Statistics from Stanford University ★★★★☆(40)
• Probability – The Science of Uncertainty and Data from Massachusetts Institute of Technology ★★★★★(33) ⭐
• Statistical Learning from Stanford University ★★★★☆(28)
• Fundamentals of Statistics from Massachusetts Institute of Technology ★★★★★(9)
• Introduction to Probability and Data with R from Duke University ★★★★☆(6)
• Fat Chance: Probability from the Ground Up from Harvard University ★★★★★(5)
• Statistical Inference and Modeling for High-throughput Experiments from Harvard University ★★★★★(4)
• Introduction to Probability: Part 1 – The Fundamentals from Massachusetts Institute of Technology ★★★★★(4)
• Computational Probability and Inference from Massachusetts Institute of Technology ★★★★★(3)
• Statistics: Unlocking the World of Data from University of Edinburgh ★★★★☆(2)
• Introduction to Probability from Harvard University ★★★★★(1)
• A Crash Course in Causality: Inferring Causal Effects from Observational Data from University of Pennsylvania ★★★★☆(1)
• Hypothesis Testing in Public Health from Johns Hopkins University ★★★★★(1)
• Summary Statistics in Public Health from Johns Hopkins University ★★★★★(1)
• Multiple Regression Analysis in Public Health from Johns Hopkins University ★★★★★(1)
• Introduction to Statistics & Data Analysis in Public Health from Imperial College London ★★★★★(1)
• SP18: Time Series Analysis from Georgia Institute of Technology
• Bayesian Statistics from Duke University
• Introduction to Statistics from Stanford University
• Introduction to Probability Management from Stanford University
• Probabilistic Systems Analysis and Applied Probability (Fall 2010) from Massachusetts Institute of Technology
• Probabilistic Systems Analysis and Applied Probability (Fall 2013) from Massachusetts Institute of Technology
• Statistics for Applications (Fall 2016) from Massachusetts Institute of Technology
• What are the Chances? Probability and Uncertainty in Statistics from Johns Hopkins University
• Causal Inference from Columbia University
• Causal Inference 2 from Columbia University
• Inferential Statistics from Duke University
• Inferenzstatistik from Duke University
• Probability and Statistics IV: Confidence Intervals and Hypothesis Tests from Georgia Institute of Technology
• Probability and Statistics II: Random Variables – Great Expectations to Bell Curves from Georgia Institute of Technology
• Probability and Statistics III: A Gentle Introduction to Statistics from Georgia Institute of Technology
• Probability and Statistics I: A Gentle Introduction to Probability from Georgia Institute of Technology
• Selected Topics on Discrete Choice from École Polytechnique Fédérale de Lausanne
• Logistic Regression in R for Public Health from Imperial College London
• Survival Analysis in R for Public Health from Imperial College London
• Structural Equation Model and its Applications | 结构方程模型及其应用 (粤语) from The Chinese University of Hong Kong
• Structural Equation Model and its Applications | 结构方程模型及其应用 (普通话) from The Chinese University of Hong Kong

Best Algebra Online Courses (20)

• Matrix Algebra for Engineers from The Hong Kong University of Science and Technology ★★★★★(529) ⭐
• Linear Algebra – Foundations to Frontiers from The University of Texas at Austin ★★★★☆(13)
• Introduction to Linear Models and Matrix Algebra from Harvard University ★★★★☆(12)
• Algèbre Linéaire (Partie 1) from École Polytechnique Fédérale de Lausanne ★★★★★(2)
• Linear Algebra III: Determinants and Eigenvalues from Georgia Institute of Technology ★★★★★(1)
• Linear Algebra IV: Orthogonality & Symmetric Matrices and the SVD from Georgia Institute of Technology ★★★★★(1)
• Algèbre Linéaire (Partie 3) from École Polytechnique Fédérale de Lausanne ★★★★★(1)
• Algèbre Linéaire (Partie 2) from École Polytechnique Fédérale de Lausanne ★★★★★(1)
• Linear Regression in R for Public Health from Imperial College London ★★★★★(1)
• Algebra: Elementary to Advanced – Functions & Applications from Johns Hopkins University ★★★★☆(1)
• Mathematics for Machine Learning: Linear Algebra from Imperial College London
• Linear Algebra I: Linear Equations from Georgia Institute of Technology
• Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (Spring 2018) from Massachusetts Institute of Technology
• Linear Algebra (Fall 2011) from Massachusetts Institute of Technology
• Linear Algebra II: Matrix Algebra from Georgia Institute of Technology
• Advanced Linear Algebra: Foundations to Frontiers from The University of Texas at Austin
• Algebra: Elementary to Advanced – Polynomials and Roots from Johns Hopkins University
• Algebra: Elementary to Advanced – Equations & Inequalities from Johns Hopkins University
• Data Science: Linear Regression from Harvard University
• Linear Regression and Modeling from Duke University

Best Calculus Online Courses (34)

• Differential Equations for Engineers from The Hong Kong University of Science and Technology ★★★★★(263)
• Calculus: Single Variable Part 1 – Functions from University of Pennsylvania ★★★★★(8)
• Introduction to Differential Equations from Massachusetts Institute of Technology ★★★★★(7)
• Calculus 1A: Differentiation from Massachusetts Institute of Technology ★★★★★(7)
• Calculus: Single Variable Part 2 – Differentiation from University of Pennsylvania ★★★★★(5)
• Differential Equations: 2×2 Systems from Massachusetts Institute of Technology ★★★★★(5)
• Calculus: Single Variable Part 3 – Integration from University of Pennsylvania ★★★★☆(4)
• Differential Equations: Fourier Series and Partial Differential Equations from Massachusetts Institute of Technology ★★★★★(3)
• Calculus 1C: Coordinate Systems & Infinite Series from Massachusetts Institute of Technology ★★★★★(3)
• Calculus: Single Variable Part 4 – Applications from University of Pennsylvania ★★★★★(3)
• Analytic Combinatorics from Princeton University ★★★★☆(3)
• Discovery Precalculus: A Creative and Connected Approach from The University of Texas at Austin ★★★★★(3)
• Calculus Applied! from Harvard University ★★★★★(2)
• Combinatorial Mathematics | 组合数学 from Tsinghua University ★★★★☆(2)
• Engineering Calculus and Differential Equations from The University of Hong Kong ★★★★★(2)
• Calculus 1B: Integration from Massachusetts Institute of Technology ★★★★★(1)
• Calculus through Data & Modeling: Differentiation Rules from Johns Hopkins University ★★★★☆(1)
• Differential Equations (Fall 2011) from Massachusetts Institute of Technology ★★★★★(1)
• Multivariable Calculus (Fall 2007) from Massachusetts Institute of Technology
• Single Variable Calculus (Fall 2006) from Massachusetts Institute of Technology
• Multivariable Calculus 1: Vectors and Derivatives from Massachusetts Institute of Technology
• Single Variable Calculus from University of Pennsylvania
• Calculus through Data & Modelling: Vector Calculus from Johns Hopkins University
• Calculus through Data & Modelling: Integration Applications from Johns Hopkins University
• Calculus through Data & Modelling: Techniques of Integration from Johns Hopkins University
• Calculus through Data & Modelling: Series and Integration from Johns Hopkins University
• [New] Applied Calculus with Python from Johns Hopkins University
• Precalculus: Periodic Functions from Johns Hopkins University
• Precalculus: Mathematical Modeling from Johns Hopkins University
• Precalculus: Relations and Functions from Johns Hopkins University
• Calculus through Data & Modeling: Applying Differentiation from Johns Hopkins University
• Calculus through Data & Modeling: Limits & Derivatives from Johns Hopkins University
• Calculus through Data & Modeling: Precalculus Review from Johns Hopkins University
• A-Level Further Mathematics for Year 12 – Course 2: 3 x 3 Matrices, Mathematical Induction, Calculus Methods and Applications, Maclaurin Series, Complex Numbers and Polar Coordinates from Imperial College London