DATA 420 - Predictive Analytics

5 CR

Previously DA 420.
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(s): DATA 460 (or DA 460) with a C or better and DATA 333 (or ISIT 333) with a grade of C or better, or permission of the instructor.

Course Outcomes
- 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

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