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Overview of data structures, data lifecycle, statistical inference. Data management, queries, data cleaning, data wrangling. Classification and prediction methods to include linear regression, logistic regression, k-nearest neighbors, classification and regression trees. Association analysis. Clustering methods. Emphasis on analyzing data, use and development of software tools, and comparing methods.

Prerequisite: (²Ñ´¡Ìý305 or ²Ñ´¡Ìý405) and (ST 305 or ³§°ÕÌý312 or ³§°ÕÌý370 or ³§°ÕÌý372 or ST 380) and (°ä³§°äÌý111 or °ä³§°äÌý112 or °ä³§°äÌý113 or CSC 114 or °ä³§°äÌý116 or ³§°ÕÌý114 or ³§°ÕÌý445)

Typically offered in Fall only