Course Description
Basic principles and techniques of data analytics, data wrangling, and visualization. Statistical learning techniques, including (a) supervised learning topics like regression and classification, (b) unsupervised learning techniques like clustering and principal component analysis, (c) an introduction to deep learning, and (d) an Introduction to text mining and natural language processing. The concepts will be implemented with R, and the focus will be on practical case studies with real-world datasets.
Spring 2025
Instructors
Meeting Patterns
Classes Start:
January 6, 2025
Classes End:
April 22, 2025
Location:
02235 SAS Hall
Class Days:
M W
Class Start Time:
4:30pm
Class End Time:
5:45pm
Class Type:
Lecture
Credits:
3.00
Restrictions:
Prerequisite: ST 430 and (ST 308 or ST 114 or CSC 111 or CSC 116 or ISE 135)