Time-to-event data are common in biomedical research and present unique challenges for analysis, given many subjects under study will not experience the event of interest. After outlining the basic structure of survival data, we will the cover the key methods for survival analysis including Kaplan Meier survival curves; the log-rank test and alternative testing procedures for group comparisons; and the Cox proportional hazards model. Analytical approaches for survival data with competing risks will also be introduced. Emphasis in this module will be on the practical application of these methods, with illustrative examples from medical and public health research being used throughout. Examples will feature best practices for reporting of results and analysis pitfalls to avoid. As time allows, we will consider concepts and controversies for survival analysis estimands and fundamental issues for study design and power. All examples will be conducted using R.