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Principles of statistics applied to analysis of biological and health data, evaluation of public health and clinical programs. Gen Ed: R2 (Analytical Reasoning).
R has emerged as a preferred programming language in data science. This course covers topics in R programming to develop powerful, robust, and reusable data science tools. Main topics include data wrangling, visualization and modeling; advanced programming; constructing R packages with documentation and unit test functions. We will use base R and a range of R packages from the tidyverse family. Workflow in data science projects, reproducible research, and how to use Github for collaborative projects will also be discussed. Students will gain hands-on experience through in-class coding activities, homework assignments and a final group project which will involve building an R package from scratch.
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. While 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 run programs and learn how to interpret their outputs for analysis of time-to-event data. Among many statistical methods, particular emphasis will be on the proportional hazards model, which is the most widely used regression model for time-to-event data.
This course provides an introduction to statistical computing with the R programming language. Students will learn how to efficiently manage, analyze, and visualize data using R.