Data Visualization
This graduate course focuses on the design of visual representations of data, using the framework presented in Visualization Analysis & Design, in order to discover patterns, answer questions, convey findings, drive decisions, and provide persuasive evidence. Throughout the course, students complete hands-on exercises to apply the research and design concepts they are learning using relevant programming libraries and software tools, including Observable and Colab notebooks, D3, Vega-Lite, Altair, and Tableau, among others.
Regression Analysis
This graduate course is intended to further students' exposure to regression techniques from a simulation-based perspective, with an emphasis on applications rather than mathematical theory. Topics include: linear regression with a single predictor and multiple predictors; linear regression assumptions, diagnostics, and interpretation; prediction and inference; transformations and interactions; analysis of variance (ANOVA); global tests for coefficients (F-tests); contingency tables; and information criteria and model comparison. The programming language R is used throughout the course.