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.
SML 201 is an introduction to the burgeoning field of data science, which is primarily concerned with data-driven discovery and utilizing data as a research and technology development tool. We cover approaches and techniques for obtaining, organizing, exploring, and analyzing data, as well as creating tools based on data. Elements of statistics, machine learning, and statistical computing form the basis of the course content. We consider applications in the natural sciences, social sciences, and engineering. Note: I no longer teach this course, but I will continue to make the materials available.