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CISC 251 Data Analytics Units: 3.00
Introduction to data analytics; data preparation; assessing performance; prediction methods such as decision trees, random forests, support vector machines, neural networks and rules; ensemble methods such as bagging and boosting; clustering techniques such as expectation-maximization, matrix decompositions, and bi-clustering; attribute selection.
Learning Hours: 120 (36 Lecture, 24 Laboratory, 60 Private Study)
Requirements: Prerequisite A cumulative GPA of a 1.70 or higher.
Exclusion CISC 333; CMPE 333.
Recommended Experience with problem solving in any discipline.
Offering Faculty: Faculty of Arts and Science