STATISTICAL DATA VISUALIZATION

Prerequisites & course information

Requisites: ( STAT 240 or 303 ), graduate professional/standing, or declared in Statistics VISP Course Designation: Core GenEd - Natural Science & Wellness Breadth - Natural Science Level - Intermediate L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Apply preprocessing strategies, including outlier removal, missing data imputation, and tidying, in a way that supports downstream visualization Audience: Undergraduate 2. Develop a vocabulary of visual encoding that support exploration of geospatial, temporal, tree-structured, and network data, and demonstrate facility implementing them using packages in the R programming language Audience: Undergraduate 3. Design dynamic queries that support interactive visualization of heterogenous data and demonstrate facility implementing them using the shiny package in the R programming language Audience: Undergraduate 4. Design effective visualizations to summarize the results of dimensionality reduction and clustering algorithms Audience: Undergraduate 5. Use visual artifacts derived from complex statistical machine learning models to discuss the patterns they learn and mistakes they make Audience: Undergraduate 6. Recognize chart junk in real-word visualizations and propose improved alternatives Audience: Undergraduate Learning Outcomes: 1. Apply preprocessing strategies, including outlier removal, missing data imputation, and tidying, in a way that supports downstream visualization Audience: Undergraduate 2. Develop a vocabulary of visual encoding that support exploration of geospatial, temporal, tree-structured, and network data, and demonstrate facility implementing them using packages in the R programming language Audience: Undergraduate 3. Design dynamic queries that support interactive visualization of heterogenous data and demonstrate facility implementing them using the shiny package in the R programming language Audience: Undergraduate 4. Design effective visualizations to summarize the results of dimensionality reduction and clustering algorithms Audience: Undergraduate 5. Use visual artifacts derived from complex statistical machine learning models to discuss the patterns they learn and mistakes they make Audience: Undergraduate 6. Recognize chart junk in real-word visualizations and propose improved alternatives Audience: Undergraduate