This course establishes a foundation in applied statistics, with a particular emphasis on problems in genomics. The course introduces students to key concepts and methods in applied statistics through their role in classical and modern genetics and genomics problems. Topics may include likelihood based inference, Bayesian inference, bootstrap, EM algorithm, regularization, statistical modeling, principal components analysis, multiple hypothesis testing, and causality. The statistical programming language R is used to explore methods and analyze data.
A detailed syllabus, which includes grading and administrative detials, can be found on the course Blackboard site.