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 (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
Concurrent Education Degree Requirements
...3.0, CISC 102 /3.0, CISC 203...215, PSYC 202 , PSYC 251 , PSYC 271 , PSYC...