FOUNDATIONS OF STATISTICAL LEARNING FOR BUSINESS ANALYTICS
An introduction to predictive modeling for business applications beginning with some of the foundations. Leads to development of linear regression and classification models, and discussion of building models for prediction. Topics include selection, regularization, and the bias-variance tradeoff.
Topics
- selection
- regularization
- and the bias-variance tradeoff
Topics explicitly listed in official catalogue description
Prerequisites & course information
Requisites: GEN BUS 307 , 317 , 704 , 705 , 881 , ECON 400 , 410 , STAT/MATH 310 , 410, STAT 333 , 340 , or declared in the Business Exchange program Course Designation: Core GenEd - Mathematics & Quantitative Reasoning Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Build (generalized) linear regression models for the purpose of prediction in business contexts, choosing from a variety of statistical learning techniques. Audience: Undergraduate 2. Select predictive algorithms and features for machine learning models in business settings, taking into account the fundamental bias-variance tradeoff. Audience: Undergraduate 3. Evaluate prediction accuracy of models for regression and classification problems for business applications. Audience: Undergraduate 4. Communicate the advantages and disadvantages of different prediction approaches, and justify and explain their choices in the context of business problems. Audience: Undergraduate 5. Interpret the prediction outcomes of a given model in a business context, and communicate the results to non-expert audiences. Audience: Undergraduate Learning Outcomes: 1. Build (generalized) linear regression models for the purpose of prediction in business contexts, choosing from a variety of statistical learning techniques. Audience: Undergraduate 2. Select predictive algorithms and features for machine learning models in business settings, taking into account the fundamental bias-variance tradeoff. Audience: Undergraduate 3. Evaluate prediction accuracy of models for regression and classification problems for business applications. Audience: Undergraduate 4. Communicate the advantages and disadvantages of different prediction approaches, and justify and explain their choices in the context of business problems. Audience: Undergraduate 5. Interpret the prediction outcomes of a given model in a business context, and communicate the results to non-expert audiences. Audience: Undergraduate
Sources
- Course catalogue ↗2026-2027