Introduction to computational and mathematical tools of data science. Cover statistical estimation and optimization algorithms, neural networks, geometry of high dimensional spaces, randomized methods, sparse approximation, and dimension reduction techniques. Three lecture hours a week for one semester. Computational Science, Engineering, and Mathematics 382M and 383M may not both be counted. Prerequisite: Graduate standing.