DA 420 Predictive Analytics • 5 Cr.
Students will study the process of formulating business objectives, data selection, preparation, and partition to successfully design, build, evaluate, and implement predictive models for a variety of practical business applications. Topics include a variety of predictive models such as classification, decision trees, machine learning, supervised and unsupervised learning. Prerequisite: MATH 342 with a C or better, or permission of the instructor. Recommended: DA 460.
After completing this class, students should be able to:
- Identify the common predictive analytics techniques, and their advantages and limitations. - Identify common predictive models and classifiers and their applications. - Evaluate the relevant aspects of a real world data set and choose an appropriate
- Winter 2020 (current quarter)