DATA 520G - Seminar: Data Mining and Multivariate Statistics

A seminar-style exploration of techniques used for massive data sets, with an emphasis on the overlap between the areas of mathematics, statistics and computer science. Techniques will include cluster analysis, principal components, and logistic regression, and more, with an emphasis on practical data modeling that connects statistical reasoning to mathematical (matrix) structural thinking to computational issues such as complexity and distributed computing.

Credit(s): 3

Repeatable for Credit? No
Disciplinary Perspective(s):
None
Interconnecting Perspective(s):
None
University Graduation Requirement(s):
None

Prerequisite(s): (DATA 222 - Data Science or STAT 250 - Statistical Computing or CS 260 - Object-Oriented Programming and Design)
AND (CS 170 - Introduction to Computer Science I or CS 180 - Foundations of Computer Science I)
AND (CS 291 - Models of Computation or MATH 357 - Linear Algebra or MATH 525 - Mathematics for Data Science or MATH 525G - Mathematics for Data Science).