Teaching

Fall '26

BIOSTAT521 Applied Biostatistics: Students will learn the basic steps in analyzing public health data, from initial study design to exploratory data analysis to statistical inference. We will cover graphical representations and descriptive statistics for univariate and multivariate data, sampling distributions for statistics, hypothesis testing (including t-tests and chi-square tests), construction of confidence intervals, analysis of contingency tables, and simple and multiple linear regression. Students will learn to apply the concepts covered in class through a semester-long hands-on analysis of public health data using the statistical software R. This course is hosted on Canvas and taught in person.

The Why of Statistics: Critical Thinking with Data and How to Reason Under Uncertainty: Designed for grades 9-12 teachers across Math, Science, and Social Studies content areas, this year-long program supports educators in building their own data fluency while creating classroom-ready activities that develop students’ critical thinking with data. This professional development program will explore the essential role of statistics and critical thinking in data science — not just how to analyze data, but how to reason through uncertainty, ask better questions, and make evidence-based claims. This course is hosted on Google Drive and taught in person and virtually.

ST790 Statistical Methods for Data Integration: This course covers statistical methods for integrating information from multiple data sources. It begins with an introduction of the classical meta-analysis, dating to the 1970's, and traces the evolution of the core techniques of data integration through to recent decades. Then, it covers modern approaches aimed at relaxing the strong assumptions of the original data integration framework. Emphasis is placed on both theoretical and computational aspects. This course is hosted on Moodle and taught in person.

ST422 Introduction to Mathematical Statistics II: Second of a two-semester sequence of mathematical statistics, primarily for undergraduate majors in Statistics. Random samples, point and interval estimators and their properties, methods of moments, maximum likelihood, tests of hypotheses, elements of nonparametric statistics and elements of general linear model theory. This course is hosted on Moodle and taught in person. See also the course catalog.

ST502 Fundamentals of Statistical Inference II: Second of a two-semester sequence in probability and statistics taught at a calculus-based level. Statistical inference: methods of construction and evaluation of estimators, hypothesis tests, and interval estimators, including maximum likelihood. This course is hosted on Moodle. See also the course catalog.