Essential Linear Algebra for Data Science

The Linear Systems and Gaussian Elimination module introduces you to the course, defining linear systems and exploring three solution options, coordinate system visualization, and the rules of Gaussian Elimination. Through practical examples, you'll gain a solid understanding of solving linear systems and Gaussian Elimination, preparing you for real-world problem-solving scenarios.

The Matrix Algebra module delves into the essentials of matrix algebra, covering topics such as matrix multiplication, the identity matrix, and scale. You'll also have the opportunity to test your knowledge through quizzes, ensuring a comprehensive understanding of these fundamental concepts.

The Properties of a Linear System module delves into vectors, coordinates, linear combinations, spans, linear independence, linear transformations, and matrix inverses. Through examples and quizzes, you'll gain a profound understanding of these crucial concepts, setting the stage for advanced applications in data science.

The Determinant and Eigens module provides an in-depth exploration of determinants, inverses of matrices, eigenvalues, and eigenvectors. By mastering these concepts, you'll be well-equipped to analyze and manipulate data using advanced linear algebra techniques.

The Projections and Least Squares module covers transpose, inner product, unit vectors, orthogonal vectors, orthogonal projections, and least squares. Through practical examples and a final exam, you'll solidify your understanding of these concepts, preparing you for real-world data science challenges.