Udemy · Sahib Maker
CFA Level I Quantitative Methods — Sahib Maker
Explore Sahib Maker’s CFA Level I quantitative-methods walkthroughs for returns, probability, and regression, with scope and exam-cost distinctions.
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.
Udemy · Sahib Maker
Explore Sahib Maker’s CFA Level I quantitative-methods walkthroughs for returns, probability, and regression, with scope and exam-cost distinctions.
Coursera · University of Colorado Boulder — James Bird
Colorado Boulder offers a focused linear algebra course covering elimination, matrix properties, eigenvectors, projections and least squares.
Coursera · Howard University — Dennis Davenport
Practice inverses, matrix arithmetic, row reduction and eigenvectors with Howard University’s Python-based linear algebra course on Coursera.
Coursera · University of Geneva — Stefan Andreas Sperlich
Explore Geneva’s statistics course for constructing composite indices and interpreting international comparisons, with examples from global organizations.
Coursera · University of Illinois Urbana-Champaign — Fataneh Taghaboni-Dutta
Compare Illinois’ business-statistics course for hypothesis tests, two-sample inference and linear regression, with Excel used for analysis.
Coursera · The University of Sydney — David Easdown
Explore Sydney’s calculus course for precalculus, functions, derivatives and integration, connecting foundational mathematics with applications.
Coursera · University of California San Diego
A puzzle- and programming-based introduction to proofs, combinatorics, probability, graph theory, and mathematical reasoning for computing.
Coursera · The University of Sydney — David Easdown
David Easdown develops geometric vectors, systems, matrices and eigenvectors in Sydney’s substantial introductory linear algebra course.
Coursera · Howard University — Dennis Davenport
Howard University introduces matrices, vector equations and Python notebooks, connecting elementary linear algebra with practical data problems.
Khan Academy
A free self-paced statistics and probability sequence with explanations and exercises for describing data, chance, and statistical reasoning.