CIS 3920
Data Mining for Bus Analytics · 3 credits · Fall 2026
Requirements
Pre-requisite: STA 3000 or CIS 2300 or MTH 3300
About this course
This course provides students with an overview of machine learning techniques, focusing on practical applications in business analytics. Students begin with the basics of data exploration and data preparation. Using practical examples and hands-on learning, the students will then engage in model selection, training, assessment as well as validation to solve problems in a range of business domains. The course covers a range of techniques including linear regression, logistic regression, adaptive boosting, decision trees, random forests, K-Nearest neighbors, and support vector machines. The course also introduces students to the basics of contemporary model architectures (e.g., neural network and generative AI). The course equips students with both theoretical knowledge and practical skills essential for addressing real-world challenges in business analytics.
Seat status as of Sep 28, 10:49 PM ET. Seats change fast, so a class shown as open may be full by now. Watch a full class to be emailed the moment it opens.
Chill Rank compares professors using Rate My Professors reviews, from Chill to Brutal. What each rank means