DA 375 - Statistical Techniques for Data Analytics

3 Cr. Hrs.

In this course, students will study some of the most commonly used techniques of statistical learning in data analytics. Students will learn to distinguish supervised learning methods such as regression, classification, and tree-based methods for both, from unsupervised learning techniques such as clustering and association algorithms. They will develop skills in determining which techniques are appropriate for answering a given question about a particular set of data, practice application to data using a statistical programming language, learn how to assess model accuracy and validity, and learn how to interpret their results.

Prerequisite(s): BUS 230 or MAT 280
Grading Basis: Graded Repeatable: No Typical Periods Offered: Spring
Formerly: DA 399