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Viewing: DNSC 3402 : Data Mining

Last approved: Wed, 17 Aug 2016 08:06:57 GMT

Last edit: Sat, 13 Aug 2016 22:58:47 GMT

Catalog Pages referencing this course
School of Business
Decision Sciences (DNSC)
DNSC
3402
Data Mining
Data Mining
Fall 2016
0,3
Course Type
Discussion Group
Lecture
Default Grading Method
Letter Grade

No
No
APSC 3115 or STAT 1051 or STAT 1053 or STAT 1111; Math 1231 or Math 1252
Corequisites

25
Prasad
Frequency of Offering

Term(s) Offered

Are there Course Equivalents?
No
 
No
Fee Type


No


The practice of exploring and discovering actionable business intelligence from large amounts of data; concepts, methods, and tools; supervised and unsupervised data mining techniques for discovering relationships in large data sets and building predictive models; regression models, decision trees, neural networks, clustering, and association analysis.
• Be able to determine what data mining methods would be the most appropriate for the most common data analytic problems that arise in business • Develop a good understanding of Supervised and Unsupervised learning techniques • Learn how to build and assess different types of predictive models • Learn how to build models using real data using standard data mining software tools (XL Miner, SAS JMP, SAS Enterprise Miner)
Uploaded a Course Syllabus

Course Attribute

Updating Prerequisites Only
Key: 9851