Explore linear algebra, discrete mathematics, and statistics resources, with advice on prerequisites, problem solving, and explaining each reasoning step.
Mathematics develops through solving problems and explaining why an approach works. Different branches support different goals: linear algebra connects with data and systems, discrete mathematics with computer science, and statistics with uncertainty. Choose a route that matches the reasoning you need.
Check algebra and calculus prerequisites before starting. A lecture can make a solution appear clear even when reproducing it independently is difficult. Favor material with exercises, worked explanations, and opportunities to examine the assumptions behind a theorem or statistical result.
Attempt a problem before reading the solution, identify the first step you cannot justify, and review that idea directly. Explain the result in words as well as symbols. Returning to a prerequisite is often the fastest way forward.
Coursera · DeepLearning.AI — Luis Serrano
Luis Serrano connects linear systems, vector transformations and eigenvectors with Python exercises for machine learning and data science.
Coursera · Johns Hopkins University — Joseph W. Cutrone, PhD
Study linear systems, vectors and matrix equations with Johns Hopkins, including row reduction and the meaning of existence and uniqueness.
Coursera · Johns Hopkins University — Joseph W. Cutrone, PhD
Explore matrix algebra, subspaces, determinants and diagonalization in the second course of Johns Hopkins’ linear algebra sequence.
Coursera · Johns Hopkins University — Joseph W. Cutrone, PhD
Johns Hopkins develops orthogonal projections, least squares and symmetric matrices in an intermediate course on geometry and linear algebra.
Coursera · Imperial College London — Samuel J. Cooper
Review Imperial’s calculus course for multivariable derivatives, the chain rule, Taylor approximations, optimization and regression.
Coursera · KAIST — Yoon Yong Jin
KAIST links elementary linear algebra with AI applications, covering combinations, inverses, determinants and eigenvalue-based transformations.
MIT OpenCourseWare · Gilbert Strang, Massachusetts Institute of Technology
Gilbert Strang's MIT linear algebra course on systems of equations, vector spaces, matrices, eigenvalues, and geometric interpretation.
Coursera · University of London — Robert Zimmer
Explore London’s course for means, deviations, two-dimensional data, pandas and k-means clustering, with a final data-clustering project.
Coursera · Macquarie University — Assoc Prof Prashan S. M. Karunaratne
Explore Macquarie’s introductory statistics course for variation, normal distributions and inference, using Excel without earlier statistics training.
Coursera · Macquarie University — Assoc Prof Prashan S. M. Karunaratne
Review Macquarie’s intermediate Excel statistics course for pivot tables, categorical tests, two-sample inference, correlation and regression.