This course introduces first-semester Master in Interdisciplinary Data Science students to principles of statistical thinking, linear, and generalized linear models. We emphasize making careful and thoughtful decisions with real-world data.
STA 540L: Case Studies (Spring 2026)
Master of Statistical Science students apply statistical analysis skills to in-depth data analysis projects in a variety of areas of application. Students design and implement a data analysis plan based on substantive questions or hypotheses and communicate their results both technically and non-technically in oral presentations and written reports.
This course introduces key statistical concepts and methods used in health data science, with a focus on practical applications to real-world health research. We explore the diverse sources of health data and cover analytic methods, including survival analysis, survey data analysis, and causal inference. Emphasis will be placed on translating research questions into rigorous statistical analyses, addressing issues of bias and confounding, and effectively communicating findings. Students gain experience working with complex health datasets and applying modern statistical tools to inform evidence-based decision-making in health care and public health. This course is available to graduate and advanced undergraduate students.
Introduction to statistics as a science of understanding and analyzing data. Themes include data collection, exploratory analysis, inference, and modeling. Focus on principles underlying quantitative research in social sciences, humanities, and public policy. Research projects teach the process of scientific discovery and synthesis and critical evaluation of research and statistical arguments.