Statistical Learning for Data Analysis (IEMS-304, Northwestern)

Required undergraduate course on predictive modeling in data science. Syllabus:

Objectives:

• Understand common data structures in modern predictive and explanatory modeling in business, engineering, and science and how to formulate the appropriate solutions.
• Learn R software basics and how to use it for various regression and classification problems.
• Develop ability to fit appropriate linear and logistic models, including model selection and model diagnostics.
• Develop ability to interpret fitted linear and logistic regression models for both explanatory and predictive purposes.
• Learn concept in regression and classification with nonlinearity, including maximum likelihood estimation, cross-validation, ridge and lasso.
• Learn how to fit and interpret popular supervised learning models including trees, smoothers, nearest neighbors, random forests, and boosted trees.