CRN 52237

3.0 credit hours

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 itemsets 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 RStudio 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 or STAT 5531. Cross Listing(s): STAT 5660, MATH 5660, MATH 5660G.