Applied Data Analysis

IE 451 is about learning to pick the right statistical model for messy real-world data and knowing why it works, with the bias-variance trade-off as the recurring lens for every method you meet. You will work in R across the ISLR toolkit, fitting regressions, GLMs, splines, trees, random forests, and clustering methods to datasets like credit defaults, spam inboxes, and baseball salaries. It sits where introductory statistics hands off to applied machine learning, giving IE students the modeling fluency they need for analytics-heavy electives, thesis work, and any downstream role that involves drawing decisions from data.