Teaching
The most rewarding part of my job? Definitely teaching.
Courses
I’m lucky to work in a department that’s always innovating, both in the way we teach and what we teach. We continually tweak our existing curriculum and develop new courses that reflect the ever-evolving fields of statistics and data science.
The courses I teach at Macalester are listed below. I’m always open to new ideas and discussing materials. Please contact me!
- COMP/STAT 112: Introduction to Data Science
An intro to Data Science that was co-developed by a team of statisticians, computer scientists, and applied mathematicians. Topics span data wrangling, visualization, acquisition, and communication.
- STAT 155: Introduction to Statistical Modeling
A modern alternative to the traditional introductory course. By emphasizing statistical literacy and computing, STAT 155 covers traditional topics (eg: confidence intervals and hypothesis tests) in the multivariate regression modeling context.
Most recent course manual.
- STAT 253: Statistical Machine Learning
A sequel to STAT 155. Topics include supervised learning techniques (including regression and classification) and unsupervised learning techniques (including clustering and dimension reduction).
Most recent course manual.
- MATH/STAT 354: Probability
An introduction to probability theory and application. Fundamental topics include set theory, combinatorics, conditional probability, random variables, probability distributions, expectation, variance, moment-generating functions, and limit theorems. Special topics vary and include computer simulation, stochastic processes, and advanced applications.
- STAT 454: Bayesian Statistics
I developed Macalester’s first Bayesian course. Course topics include Bayesian philosophy, posterior inference, hierarchical models, and MCMC computing techniques. Please see the Bayes' Rules! section for information about a book I’m co-writing for courses such as mine.
Most recent course manual.
- MATH/STAT 455: Mathematical Statistics
An important course for students considering graduate work in statistics or biostatistics, MATH/STAT 455 explores the mathematics underlying modern statistical applications. Topics include: classical techniques for parameter estimation and evaluation of estimator properties, hypothesis testing, confidence intervals, and linear regression. Special topics vary and include: tests of independence, resampling techniques, introductory Bayesian concepts, and nonparametric methods.
Workshops
Beyond the undergraduate classroom, I’ve offered several workshops through the following organizations (and more).