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. Only one of the following may be counted: Computer Science 395T (Topic 10), 395T (Topic: Fdtnl Tchnq Mchn Lrng/Data Sci), Mathematics 393C (Topic: Fdtnl Tchnq Mchn Lrng/Data Sci), Computational Science, Engineering, and Mathematics 382M, 383M. Three lecture hours a week for one semester.