CISC 251 Data Analytics
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).
Concurrent Education Degree Requirements
https://www.queensu.ca/academic-calendar/education/concurrent-education-program/degree-requirements/
...3.0, CISC 102 /3.0, CISC 203...215, PSYC 202 , PSYC 251 , PSYC 271 , PSYC...
Academic Programs
https://www.queensu.ca/academic-calendar/arts-science/academic-programs/
...BLCK CANC; CHEM; CHIN; CISC; CLST; COCA; COGS...225 ; KNPE 227 ; KNPE 251 ; KNPE 254 ; KNPE...