DSI-6050 Predictive Analytics 2 - Neural Nets and Regression - with R

In this course, students will continue work from Predictive Analytics 1, and be introduced to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining. Predictive modeling takes data where a variable of interest (a target variable) is known and develops a model that relates this variable to a series of predictor variables, also called features. Four modeling techniques will be used: linear regression, logistic regression, discriminant analysis, and neural networks. The course includes hands-on work with R, a free software environment with capabilities for statistical computing. Graduate students enrolled in this course will complete a project/assignment that engages in higher levels of thought and creativity, requiring them to demonstrate knowledge at more advanced taxonomical levels.

Credits: 3

Offered in Semester, Jul 2024, Nov 2024, Mar 2025

Preview the Online Syllabus 
(Please visit the University bookstore to view the correct materials for each course by semester as the contents of the actual online syllabus may differ from the preview due to updates or revisions)