This book takes a deep dive into several key linear algebra subjects as they apply to data analytics and data mining. The book offers a case study approach where each case will be grounded in a real-world application.

Dr. Crista Arangala is Professor of Mathematics and Chair of the Department of Mathematics and Statistics at Elon University in North Carolina. She has been teaching and researching in a variety of fields including inverse problems, applied partial differential equations, applied linear algebra, mathematical modeling and service learning education. She runs a traveling science museum with her Elon University students in Kerala, India. Dr. Arangala was chosen to be a Fulbright Scholar in 2014 as a visiting lecturer at the University of Colombo where she continued her projects in inquiry learning in Linear Algebra and began working with a modeling team focusing on Dengue fever research. Dr. Arangala has published several textbooks that implore inquiry learning techniques including Exploring Linear Algebra: Labs and Projects with MATLAB® and Mathematical Modeling: Branching Beyond Calculus.

1 Graph Theory. 2. Stochastic Processes. 3. SVD and PCA. 4. Interpolation. 5. Optimization and Learning Techniques for Regression. 6. Decision Trees and Random Forests. 7. Random Matrices and Covariance Estimate. 8. Sample Solutions to Exercises.