This course provides an introduction to the analysis of time-to-event data which are commonly encountered in biomedical research and public health studies. Both applied materials and theoretical details will be covered in this course. Although the main focus is on application of the statistical methods, you will also learn their theoretical derivations as well as the underlying model assumptions. For application, we will use R to analyze time-to-event data and learn how to interpret their outputs.

- Teacher: Chi Hyun Lee

The goal of this 3-credit course is to introduce fundamentals of probability theory, statistical inference tools and their application to biostatistics. The course is intended for first-year graduate students in Biostatistics MS program and students who are interested in learning probability and statistical inference. The topics in this course include basic concepts of probability, random variables, important probability distributions (e.g., normal, exponential, binomial and Poisson), marginal distribution, conditional distribution, joint distribution, expectation and variance, conditional expectation, law of large numbers, central limit theorem, sampling distributions, point estimation, maximum likelihood estimation, method of moments and estimating equations, interval estimation, hypothesis testing. Examples from biomedical applications will be used whenever possible. Simple simulations with R software will be used to illustrate some concepts in probability and statistical inference.

- Teacher: Jing Qian

R has emerged as a preferred programming language in data science. This course covers advanced topics in R programming to develop powerful, robust, and reusable data science tools. By the end of this course, students should be able to use git and GitHub for version control and collaboration, organize statistical programming and data analysis projects into R packages, and make code robust with informative error messages and unit testing.

- Teacher: Raji Balasubramanian

R has emerged as a preferred programming language in data science. This course covers intermediate topics in R programming to develop powerful, robust, and reusable data science tools. Main topics include programming, iteration, modeling, and building wen-based tools to deliver your data products using R Shiny.

- Teacher: Raji Balasubramanian

R has emerged as a preferred programming language in data science. This course covers an introduction to topics in R programming to develop powerful, robust, and reusable data science tools. Main topics include importing of data, data wrangling, visualization, and reporting.

- Teacher: Raji Balasubramanian

Major designs used in clinical investigations; alternative approaches to the analysis of gathered data. Prerequisite: BIOST&EP 640.

- Teacher: Scott Chasan-Taber