This course introduces students to various computationally intensive statistical techniques. Topics will include numerical optimization for statistical inference (grandlent- based optimization, the Expectation-Minimization (EM) lgorithm, and Fisher scoring), random number generation, resampling methods such as the bootstrap, permutation and randomization tests, cross-validation, Markov Chain Monte Carlo techniques (Gibbs sampling and Metropolls-Hastings algorithm), and nonparametric curve fitting. Students will learn to apply these techniques to solve data science problems using the statistical software R. Prerequisite: STAT 328 or Stat 334. (F;S;SS)