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
https://www.queensu.ca/academic-calendar/education/concurrent-education-program/degree-requirements/
...0, CISC 101 /3.0, ECON 250 /3.0, GPHY 247 /3.0, KNPE 251...