Course Description
The course covers foundational mathematical concepts fundamental to data science and data-driven mathematical modeling. The course includes the following topics: introductory probability and vector calculus, theory for classification algorithms, linear and parametric classifiers, unsupervised and clustering methods, decision trees and ensemble methods. The focus is on applying mathematical concepts to data science methods. The course includes an introduction to Python, but some familiarity with programming is strongly recommended. Basic programming proficiency (Python preferred).
Fall 2024
Instructors
Meeting Patterns
Classes Start:
August 19, 2024
Classes End:
December 3, 2024
Location:
00102 David Clark Labs
Class Days:
T H
Class Start Time:
10:15am
Class End Time:
11:30am
Class Type:
Lecture
Credits:
3.00
Restrictions:
Prerequisite: MA 242 and (MA 303 or MA 305 or MA 405)