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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).

Prerequisite: ²Ñ´¡Ìý242 and (²Ñ´¡Ìý303 or ²Ñ´¡Ìý305 or ²Ñ´¡Ìý405)

Typically offered in Fall and Spring