DATA 520 - Data Mining and Multivariate Statistics

An exploration of techniques used for very large data sets, such as cluster analysis, principal components/factor analysis, and discriminant analysis/regression, with an emphasis on statistical reasoning that connects mathematical structures from matrix algebra to computational issues such as computational complexity and distributed computing.

Credit(s): 3
Prerequisite(s): (STAT 220 or DATA 222 - Data Science or STAT 250 - Statistical Computing or CS 260 - Object-Oriented Programming and Design)
AND (STAT 370 - Probability or STAT 290 - Statistics)
AND (CS 170 - Introduction to Computer Science I or CS 180 - Foundations of Computer Science I)
AND (MATH 285 - Matrix Algebra or MATH 357 - Linear Algebra).
Disciplinary Perspective(s):
None
Interconnecting Perspective(s):
None
University Graduation Requirement(s):
None