MA477 Course Description

This course builds on the foundations presented in the core probability and statistics course and the applied statistics course to develop a broad base of Advanced Data Science to some of the most common techniques in the field. The mathematical basis for each method is presented with focus on both the statistical theory and application. Topics covered may include classification and regression trees, regularization methods, splines and localized regression, and model validation.

Assigned reading and homework below reference each of the three course textbooks with the following convention:

• D: Intro to Python textbook (by Deitel)

• G: Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow (by Geron)

• I or ISLR: An Introduction to Statistical Learning with Applications in R, 2nd Edition (by James, Witten, Hastie, and Tibshirani)

Chapter number is typically given first followed by assigned problems (i.e. D4-5-8 would be Chapter 4, problems 5, 6, 7, and 8).