Academic Calendar 2021-2022

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CISC 251 Data Analytics

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 (36L;24Lb;60P).

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  

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