teaching
Courses taught at UT Austin.
PSY 371E · Foundations of Psychological Data Science I
Students learn to load, clean, reshape, and visualize data with pandas and seaborn, then move from description to modeling: distributions, correlation, simple and multiple regression, and confounding. Each module pairs a guided tutorial with mini-projects on real data, from film representation to concussion and reaction time. The course ends with an individual project: a pitched and peer-reviewed proposal, a final analysis notebook, and a presentation.
PSY 371F · Foundations of Psychological Data Science II
Builds on the first course toward machine learning for the behavioral sciences. Students begin with supervised learning and cross-validation, then work through the bias-variance trade-off, tree-based models and how to interpret them, unsupervised methods such as clustering and principal components analysis, and neural networks for images and text. The math (probability, functions, vectors) comes in as needed, but the course is built around projects on real behavioral data. Students finish with a machine learning project of their own.
Earlier courses (Arizona State University)
- Behavioral Data Science I. Data science for social and behavioral scientists in R, from wrangling and visualization to linear models and tidymodels.
- Cognitive Science. How the mind works, across philosophy, computer science, anthropology, neuroscience, and psychology.
- Language & Social Interaction. Language in its natural environment: dialogue, convergence, perspective-taking, and deception.
- Sensation & Perception. How the senses turn light, sound, and touch into experience: vision, audition, and the other senses, from psychophysics to the brain.