This course covers both theoretical and practical issues of statistical computing and algorithms required in modern data science. Students will learn how to develop algorithms for statistical methods and apply them to data science problems Topics include resampling methods, EM algorithms, Markov chain Monte Carlo simulation, gradient-based optimization, and nonparametric curve fitting. Prerequisite: STAT 707. (F;S;SS)