Linear Algebra Foundations for Data Science and Engineering

Linear algebra is the mathematical engine powering modern data science, machine learning algorithms, and engineering computations. Understanding these core concepts is essential for anyone looking to analyze high-dimensional data or build predictive models. This text-based course guides you from absolute beginner to a confident practitioner of fundamental linear algebra. You will learn how to read, interpret, and manipulate mathematical structures, preparing you to understand the inner workings of modern algorithms and vector databases. What you'll learn: - Understand foundational definitions of vectors, matrices, and systems of linear equations. - Perform core matrix operations including multiplication, transposition, and inversion. - Explore linear transformations and how they map data across different dimensions. - Grasp eigenvectors and eigenvalues and their critical role in dimensionality reduction. - Apply linear algebra concepts to modern data science scenarios, such as vector embeddings and high-dimensional spaces. - Practice solving practical algebraic problems through step-by-step written exercises. The course begins with essential terminology and basic definitions before moving systematically through vectors, matrices, and system solutions. You will read clear explanations and work through written scenarios designed to build your mathematical intuition. Designed specifically for beginners, aspiring data scientists, and entry-level engineers, this course requires no prior advanced math background. Start reading today to unlock the mathematical foundations of modern technology.