MATH 443/543 Data Analysis

Credits: (3-0) 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 or permission of instructor.
Notes: Students enrolled in MATH 543 will be held to a higher standard than those enrolled in MATH 443.

Check course scheduling information