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DANA 4830: Dimension Reduction & Classification I

Course FormatLecture 4.0 h + Seminar 0.0 h + Lab 0.0 h
Credits3.0

A core requirement in data analytics is the classification of a large group of records (items or objects) into different subgroups based on statistical criteria. The classification can be made easier if the number of dimensions of the data used is reduced. Students learn a number of techniques in reducing the number of dimensions in a data set without losing its latent structure. They also learn how to perform statistical classification into pre-defined groups. Topics include principal component analysis, factor analysis, multiple correspondence analysis, multivariate discriminant analysis, as well as stepwise techniques in regressions. Prerequisites: A minimum "C" grade in DANA 4810 and 4820.

Registration in this course is restricted to students admitted to the Post-Degree Diploma in Data Analytics and Post-Degree Certificate in Data Analytics.


Course Attributes:
2nd Year Arts
2nd Year Science
Humanities
Lab Science
Science
Social Science
University
Transferable
Other Attributes:
Offered online:No
Preparatory course:No
Repeat limit2
Additional fees:$34.3
First offered:Fall 2019
Last offered:Fall 2025
Registration
restrictions:
RP
Outline(s):No outline found.
Transfer Agreements
CourseDestinationCreditStart to End
No active transfer agreements.
Inactive Transfer Agreements (click to open)
CourseDestinationCreditStart to End
No inactive transfer agreements.

Offerings of this course:

SemesterCRNSectionSeatsWaitlistDaysTimeRoomTypeInstructor
Fall 20253080700131N/A-T-R---1630-1820 Lecture. TBA
Summer 20252029000112 -T-R---1430-1620B030LectureQuynh Nguyen
Spring 20251013100111 M-W----1430-1620A322LectureQuynh Nguyen
M------1830-2125A314ExamQuynh Nguyen
Fall 2024304700014 -T-R---1630-1820B152LectureJonathan Agyeman
----F--1830-2125B152ExamJonathan Agyeman
Summer 2024207080016 -T-R---1430-1620B008LectureQuynh Nguyen
----F--1830-2125B008ExamQuynh Nguyen
Spring 2024109950016 -T-R---1830-2020B027LectureJonathan Agyeman
--W----1830-2125B029ExamJonathan Agyeman
Fall 20233035800110 -T-R---1430-1620A272LectureJonathan Agyeman
---R---1830-2125A272ExamJonathan Agyeman
Summer 20232058000118 M-W----1630-1820B153LectureQuynh Nguyen
M------1830-2125B153ExamQuynh Nguyen
Spring 2023108460019 M-W----1830-2020B010LectureJonathan Agyeman
----F--1830-2125B018ExamJonathan Agyeman
Fall 2022306700018 -T-R---1630-1820A276LectureJonathan Agyeman
----F--1830-2125B018ExamJonathan Agyeman
Summer 2022204680016 -T-R---1630-1820B153LectureQuynh Nguyen
--W----1830-2125B153ExamQuynh Nguyen
Spring 2022110600016 M-W----1630-1820B252LectureQuynh Nguyen
-T-----1830-2125B155ExamQuynh Nguyen
Fall 20213017000113 -T-R---1430-1620B023LectureQuynh Nguyen
M------1830-2125B023ExamQuynh Nguyen
Summer 202120572W018 M-W----1430-1620WWWWWWQuynh Nguyen
----F--1830-2125WWWExamQuynh Nguyen
Fall 202031539W014 -T-R---1630-1820WWWWWWQuynh Nguyen
M------1830-2125WWWExamQuynh Nguyen
Summer 202021160W013 -T-R---1630-1820WWWWWWQuynh Nguyen
-T-----1830-2125WWWExamQuynh Nguyen
Fall 2019311650011 -T-R---1630-1820B022LectureNathaniel Payne
M------1830-2125B022ExamNathaniel Payne