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.
This course may not be repeated for credit.
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.
Sections
| LEC 1 | MW 17:00 - 19:50
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| Notes:
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This course will be offered next in
Winter 2022.