MATH - 220. Introduction to Data Mathematics

3 credit(s)
This course introduces mathematical background needed for the core data analysis methods of dimension reduction, data visualization, classification and clustering. This is not primarily a course in statistics, as it approaches data problems via linear algebraic methods such as Principal Component Analysis (visualization and dimension reduction), Fisher Linear Discriminant Analysis (classification), and K-means (clustering). Case studies will provide an immersive student experience analyzing high-dimensional data problems.

Prerequisite(s): DATA 110
Attribute: ARTS

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