MATH 5660G - Statistical Data Analytics (3 Credit Hours)

Lecture Hours: 3 Lab Hours: 0
The course will apply concepts learned in diverse areas of mathematics to data analysis. Topics include clustering and classification, data cleaning, text analysis and document similarities, frequent item sets and association rules, neural networks, support vector machines, and decision trees. This class has a primary focus on the underlying mathematical theory, with a secondary focus on application. Students will be introduced to R and R Studio for data storage, manipulation, and visualization. Graduate students will complete assignments beyond the scope of the undergraduate requirements. These assignments require higher-level mastery of the subject matter and additional deliverables representative of graduate-level work, as determined by the instructor.

Prerequisite(s): A minimum grade of “C” in the following: MATH 2160, MATH 2243, and at least one of MATH 3337 and STAT 5531.
Cross Listing(s): MATH 5660, STAT 5660, STAT 5660G.
Restriction(s): NO Undergraduate Level Students