Engineering Statistics and Data Science

4-0-8 units · Graduate

Develops ideas for making principled decisions and recommendations based on data, providing an introduction to statistical inference and statistical learning. Covers data displays and summary statistics for quantitative and qualitative data, the law of large numbers for means and empirical distributions, the normal distribution and the central limit theorem, confidence intervals, statistical hypothesis tests for the population mean and differences between population means, simple and multiple regression with quantitative data, model selection, the bias-variance tradeoff, logistic regression for binary outcomes, CART, random forests, gradient boosting, and deep learning. The statistical programming language R is used for in-class demonstrations and for out-of-class assignments. Preference to first-year Leaders for Global Operations students. No required textbook.