Description: The course is designed to prepare statistics graduate students for the computational aspects of statistical research. Topics may include but are not limited to: solving linear systems, matrix factorizations, numerical integration, monte carlo integration, markov chain monte carlo, functions and objects, parallel computing, unit testing, graphics, continuous and discrete optimization, sparse matrix operations, parallel computing, R packages. The majority of instruction will be in the R programming language.