STAT 520G - 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 form matrix algebra to computational issues such as computational complexity and distributed computing.

Credit(s): 3
Prerequisite(s):

STAT 220 - Fundamentals of Data Science or STAT 250 - Statistical Computing or CS 260 - Object-Oriented Programming and Design or PDAT 610G - Introduction to Data Science 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