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DATA611

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Predictive Analytics

Haskayne School of Business HA - Haskayne School of Business

Subject

DATA - Data Science

Description

Overview of the basic concepts and techniques in predictive analytics as well as their applications for solving real-life business problems in marketing, finance, and other areas. Techniques covered in this course include: decision trees, classification rules, association rules, clustering, support vector machines, instance-based learning. Examples and cases are discussed to gain hands-on experience.

Prerequisite(s): Data Science 601, 602, 603, 604 and admission to the Graduate Diploma in Data Science and Analytics or the Master of Data Science and Analytics with a specialization in Business Analytics.

Course Attributes

Fee Rate Group(Domestic) - H, Fee Rate Group(International) -G, GFC Hours (3-0)

Courses may consist of a Lecture, Lab, Tutorial, and/or Seminar. Students will be required to register in each component that is required for the course as indicated in the schedule of classes. Practicums, internships or other experiential learning modalities are typically indicated as a Lab component.

Component

LEC

Units

3

Repeat for Credit

No

Subject code

DATA
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