TEACHING

DS 395T: Data Science for Health Discovery and Innovation (MSDS Program)

Spring 2024 and 2025, Fall 2024

This course introduces students to statistical methods used in health data science. Topics will include survival analysis, prediction, longitudinal data analysis, design and analysis of observational studies including propensity score analysis, and design and analysis of randomized studies including sample size and power calculations, intent-to-treat analysis, and noncompliance.

SDS 320E: Elements of Statistics

Spring 2024, 2025

This course provides an introduction to statistics. Subjects include probability; principles of observational study and experimental design; statistical models and inference, including the multiple linear regression model and one-way analysis of variance. R programming is introduced.

SDS 190: Readings in Statistics

Spring 2024

Faculty directed research seminar; includes readings of research papers, discussion of on-going student and faculty projects, and professional development.

SDS 313: Introduction to Data Science

Fall 2022, 2023, 2024

(SDS Majors) This course provides an introduction to the principles and practice of data science. By the end of this course, a student will be able to: gather and prepare data for analysis; create R code that is bug-free, reproducible, and open to collaboration; explore patterns in data by creating effective visuals and investigating relationships among variables; examine the role of data science in our society and identify both the powers and limitations of what can be achieved with data; recognize how their past experiences and perspectives can influence how they analyze and interpret data; discover your own best practices for learning how to become a successful data scientist.

SDS 384: Biostatistical Methods

Spring 2023

The purpose of this course is to introduce students to biostatistical methods i.e., methods commonly used for health-specific applications. At the end of this course, the students will be able to determine the appropriate method for a particular setting, how to apply the method, and how to interpret results. Equal emphasis will be given to the theoretical background of the methods and application to data. The course will cover the following topics: (1) Survival analysis, including censoring and truncation, nonparametric and parametric estimation, competing risks, and prediction; (2) Longitudinal data analysis including linear and generalized mixed effect models and generalized estimating equations; (3) Design and analysis of observational studies including propensity score analysis and mediation analysis; (4) Design and analysis of randomized studies including sample size and power calculations, intent-to-treat analysis, and noncompliance.