Basic concepts of statistics with applications in health and life sciences. Descriptive statistics, principles of statistical inference, uncertainty assessment, hypothesis testing, public health surveys, and biomedical experimental design are considered. Methods include t-tests, simple linear regression, and categorical data analysis.

Basic concepts of statistics, with applications in health and life sciences. Descriptive statistics, principles of statistical inference, uncertainty assessment, hypothesis testing, public health surveys, and biomedical experimental design are considered. Methods include t-tests, simple linear regression, and categorical data analysis.

Introductory course on AI literacy for public health and other health sciences students. Covers AI history, its evolution, key concepts, and latest applications in medicine, public health, and other health sciences. Students will gain foundational understanding and hands-on experience with Large Language Models (LLMs).

A survey of statistical methods, with applications in public health and the biological sciences, including study design and clinical trials, categorical data analysis, simple and multiple linear regression, analysis of variance, and logistic regression. Motivating examples are drawn from public health and biomedicine.

A survey of statistical methods, with applications in public health and the biological sciences, including study design and clinical trials, categorical data analysis, simple and multiple linear regression, analysis of variance, and logistic regression. Motivating examples are drawn from public health and biomedicine.

Independent, intensive, and extended research conducted under the supervision of a faculty member.

Methods for comparing time-to-event data, including univariate parametric and nonparametric procedures, regression models, diagnostics, group comparisons, and use of relevant statistical computing packages.

Faculty-supervised independent or collaborative inquiry into fundamental and applied problems within a discipline that requires students to gather, analyze, synthesize, and interpret data and to present results in writing and other relevant communication formats.

Faculty-supervised independent or collaborative inquiry into fundamental and applied problems within a discipline that requires students to gather, analyze, synthesize, and interpret data and to present results in writing and other relevant communication formats.

Faculty-supervised independent or collaborative inquiry into fundamental and applied problems within a discipline that requires students to gather, analyze, synthesize, and interpret data and to present results in writing and other relevant communication formats.

Faculty-supervised independent or collaborative inquiry into fundamental and applied problems within a discipline that requires students to gather, analyze, synthesize, and interpret data. Students will write or produce a thesis or other professional capstone product, such as a report or portfolio that describes their systematic and in-depth inquiry.

Research while enrolled for a master's degree under the direction of a faculty member.