This course serves as an introduction to advanced topics in Biostatistics. In this course, students will learn about a range of topics, including: applied Bayesian techniques, e.g. the Gibbs sampler; multiple testing adjustments for high-dimensional data; the expectation-maximization algorithm; multiple imputation for missing data; the bootstrap for hypothesis testing; and simulation techniques for characterizing algorithm performance, including power and type-1 error rates. Areas of application will include, but are not limited to, statistical genetics and genomics. This is a project-oriented course with an emphasis on statistical programming with R.