SIT717 - Enterprise Business Intelligence
|Year:||2019 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.
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%
Shmueli et al, 2016, Data Mining for Business Analytics, 3rd edition, John Wiley & Sons
Unit Fee Information
Click on the fee link below which describes you: