Scientific analysis of large data sets requires detailed knowledge of statistical methods to assess the significance of the results. This course combines computing and statistical techniques needed for advanced and high level data processing, providing the foundation material required for handling large amounts of data. The course covers computational, graphical, and numerical approaches to solve statistical problems.

Major topics covered in detail include: simulating random variables from probability distributions; visualization of multivariate data; Monte Carlo integration and variance reduction methods; Monte Carlo methods in inference, bootstrap, and permutation tests; Markov chain Monte Carlo (MCMC) methods; and density estimation. The focus will be on implementation rather than theory providing a balanced accessible introduction to statistical computing. By successfully completing this course, students should be able to develop algorithms to handle large datasets using advanced statistical techniques, and to assess the accuracy and statistical significance of the models that best describe the data.