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.

An introduction to artificial intelligence (AI) literacy for undergraduate students preparing for careers in public health and related health professions. The course develops conceptual understanding of machine learning, deep learning, and large language models; examines current AI applications and evidence in health.

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.

An introduction to artificial intelligence (AI) literacy for upper-division undergraduate students and graduate students at all levels preparing for careers in public health and related health professions. The course develops conceptual understanding of machine learning, deep learning, and large language models; examines current AI applications and evidence in health.

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.