SIT717 - Enterprise Business Intelligence
|Year||2017 unit information|
|Enrolment modes:||Trimester 2: Burwood (Melbourne), Cloud (online)|
|Assumed Knowledge:||Knowledge appropriate to the topic.|
Campus: 1 x 1 hour class per week, 1 x 2 hour practical per week.
Cloud (online): Learning experiences are via CloudDeakin. Students will have the opportunity to participate in online consultation sessions.
The unit will begin with an introduction to the standard data mining processes such as CRISP-DM, then explain the requirements of business intelligence, in the context of customer relationship management. Methods to be taught in this unit include variants of association rule discovery (for basket analysis); prediction techniques such as inductive inference of decision trees and Bayes models (for market prediction), clustering techniques such as self-organization maps (for market segmentation), but with emphasis on real world applications. A selection of recent real world business intelligence case studies will be incorporated in this unit to illustrate the introduced techniques.
Projects (30%, 50%, 20%) 100%
Unit Fee Information
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