Data Exploration and Analysis

Undergraduate

An expansion of introductory statistics topics that also provides an extensive introduction to the R programming language. A concise review of foundational statistical methods moves into topics such as bootstrapping, permutation tests, multiple regression, ANOVA, ANCOVA, generalized linear models, and logistic regression. All topics emphasize the use of appropriate statistical graphics. This course also builds a strong foundation in R, RStudio, and RMarkdown, including data cleaning, data types, and strategies for handling missing data. Lectures incorporate examples of how AI can support both R coding and data analysis. This is a highly applied course, with lectures and homework focused on performing data analysis and communicating results. Prerequisite: a 1000‑level Statistics course, AP Statistics with a score of 4 or 5, or permission of the instructor. QR HTBA