SIT717 - Enterprise Business Intelligence

Year:

2020 unit information

Enrolment modes: Trimester 2: Burwood (Melbourne), Cloud (online)
Credit point(s): 1
EFTSL value: 0.125
Assumed knowledge: Knowledge appropriate to the topic.
Prerequisite:

Nil

Corequisite:

Nil

Incompatible with:

Nil

Study commitment

Students will on average spend 150 hours over the teaching period undertaking the teaching, learning and assessment activities for this unit.

Scheduled learning activities - campus

1 x 1 hour class per week, 1 x 2 hour practical per week.

Scheduled learning activities - cloud (online)

1 x 1 hour scheduled online workshop per week.

Content

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.

Unit Fee Information

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