Crash Course on Linear Algebra for Data Science and Applied Math

Linear algebra is the hidden engine behind modern technology, driving everything from machine learning algorithms and computer graphics to advanced scientific computing. Understanding these mathematical concepts is essential for anyone looking to excel in data science, engineering, or quantitative academic fields. This text-only course guides you step-by-step from absolute beginner to confidently working with core linear algebra concepts. You will transition from simply memorizing formulas to understanding the geometric intuition and practical applications of vector spaces and matrix operations. What you'll learn: 1. Understand foundational concepts of vectors, matrices, and coordinate systems. 2. Solve systems of linear equations using Gaussian elimination and matrix inverses. 3. Explore the geometric meaning of linear transformations, determinants, and vector spaces. 4. Master eigenvalues and eigenvectors and their role in modern data analysis. 5. Apply linear algebra principles to modern concepts like dimensionality reduction and embeddings. 6. Practice your mathematical reasoning through structured written exercises and step-by-step derivations. The course begins with essential terminology, basic concepts, and foundational definitions of vectors before progressing to matrix algebra, systems of equations, and spectral theory. Designed for beginners, students preparing for exams, and aspiring data professionals, this course requires no advanced mathematical prerequisites. Start reading today to unlock the mathematical foundations of modern technology.