Data Science II: Statistical Modeling and Analysis

Catalog description

Cat. I, Units 1/3. This course focuses on model- and data-driven approaches in Data Science. It covers methods from applied statistics, optimization, and machine learning to analyze and make predictions and inferences from real-world data sets. Topics covered in this course include a brief overview of statistics and linear algebra, followed by introductory machine learning methods such as linear and nonlinear regression, classification, decision trees, and dimension reduction techniques. Data exploration, data cleaning, feature engineering, and the bias-variance tradeoff will also be covered. Students will utilize various techniques and tools to explore and understand real-world data sets from various domains. Recommended Background Data science basics equivalent to DS 1010, applied statistics and regression equivalent to MA 2611 and MA 2612, and the ability to write computer programs in a scientific language equivalent to a CS programming course at the CS 1000 or CS 2000 level are assumed.