MATH 443 Data Analysis

Credits: 3

Accessing, validating, processing, extracting, visualizing and presenting data. Exposure to managing data in a relational database management system. Looking for underlying structure in data using data reduction techniques (e.g., multidimensional scaling, principal components analysis) and, more generally, unsupervised learning techniques (e.g., clustering). Prediction using supervised learning techniques such as discriminant analysis, logistic regression and decision trees. Software may include R and some version of SQL.

Prerequisites: MATH 225
Notes: This course is cross-listed with MATH 543.

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