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.

With the recent and ongoing 'data explosion', methods to delineate causation from correlation are perhaps more pressing now than ever. This course will introduce a general framework for causal inference: 1) clear statement of the scientific question, 2) definition of the causal model and parameter of interest, 3) assessment of identifiability, 4) choice and implementation of estimators including parametric and semi-parametric methods, and 5) interpretation of findings.

The goal of this course is to prepare students with necessary computing skills for a career as a statistician or data analyst/scientist. By the end of this course, you should be able to use various tools to extract data from different sources(structure or unstructured), and transform them into forms that are ready for analysis and modeling. You will also be able to build web based tools to deliver your data products using R Shiny.

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.

The main goal of this course is to prepare students with advanced computing skills for a career as a statistician or data analyst/scientist. By the end of this course, you should be able have mastery over the fundamentals of the R programming language, including concepts such as functional programming and meta programming.

Principles of statistics applied to analysis of biological and health data, evaluation of public health and clinical programs. Gen Ed: R2 (Analytical Reasoning).

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