Udemy · Krista King
Calculus 1 — Krista King
Review Krista King’s Calculus 1 course for limits, derivatives, optimization, and related rates, with algebra prerequisites and independent problems.
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 · Krista King
Review Krista King’s Calculus 1 course for limits, derivatives, optimization, and related rates, with algebra prerequisites and independent problems.
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 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.
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.