This course is intended to introduce doctoral students to a number of advanced methods in data analysis, with the aim of providing students with the ability to recognize situations in which the use of such methods may be beneficial, knowledge of the basic methods needed to conduct analyses, and an understanding of the strengths and limitations of each method. The course covers approximately five analytic methods in a series of 2- or 3-session modules. Topics may vary slightly in different semesters; examples of the types of methods covered include propensity scores, marginal structural models, quantitative bias analysis, and Bayesian analysis. Hands-on sessions in the classroom, homework assignments, and a final data analysis project provide students with practice in the conduct of analyses using these methods.