I'm lucky to work in a department that's always innovating, not just in the way we teach but what we teach. We continually tweak our existing curriculum and develop new courses in order to 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 sharing materials. Please contact me with any questions or comments (ajohns24 at macalester dot edu).
Math 155: Introduction to Statistical Modeling
A modern alternative to the traditional introductory course. By emphasizing statistical literacy and computing, Math 155 covers both traditional topics (eg: confidence intervals and hypothesis tests) as well as more sophisticated topics in multiple regression (eg: interaction, multicollinearity, analysis of variance, analysis of covariance). Click here for my course website.
Math 155: A Civic Engagement Themed First Year Course
A special iteration of Math 155 in Fall 2014 for first year students only. With support from Macalester's Civic Engagement Center, students were matched with partnering 'client' community organizations including the Frogtown Neighborhood Association, West Side Community Organization, and Central Corridor Anchor Partnerships. Thus students discovered the usual Math 155 material through community service projects.
Math 253: Machine Learning & Statistical Computing
A sequel to Math 155. Originally an advanced multiple regression course, we have put substantial effort into modernizing the curriculum to include statistical techniques for "big data". Topics include computer-based methods of data exploration, visualization, data mining, supervised and unsupervised clustering, and other techniques central to machine learning.
Math 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.
Math 454: Bayesian Statistics
I developed Macalester's first Bayesian course. Course topics include Bayesian philosophy, posterior inference, hierarchical models, and MCMC computing techniques. With the last of these, I am able to connect my scholarship and teaching. As Bayesian statistics is rarely taught at the undergraduate level, this course is a work in progress! If you have any ideas, insights, or desire for collaboration, please contact me.
Math 455: Mathematical Statistics
An important course for students considering graduate work in statistics or biostatistics, Math 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.