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SIT717 - Enterprise Business Intelligence

Year:

2020 unit information

Important Update:

Classes and seminars in Trimester 2/Semester 2, 2020 will be online. Physical distancing for coronavirus (COVID-19) will affect delivery of other learning experiences in this unit. Please check your unit sites for announcements and updates one week prior to the start of your trimester or semester.

Last updated: 2 June 2020

Enrolment modes:Trimester 2: Burwood (Melbourne), Online
Credit point(s):1
EFTSL value:0.125
Assumed Knowledge:Knowledge appropriate to the topic.
Unit Chair:Trimester 2: Thanh Thi Nguyen
Prerequisite:

Nil

Corequisite:

Nil

Incompatible with:

Nil

Typical 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:

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.

 

These are the Learning Outcomes (ULO) for this Unit

At the completion of this Unit
successful students can:

Deakin Graduate Learning Outcomes

ULO1

 develop in-depth knowledge about cutting edge topics in data mining and  business intelligence, particularly those relating to enterprise and potential skill requirements

GLO1: Discipline-specific knowledge and capabilities

ULO2

 apply predication model and its building methods

GLO1: Discipline-specific knowledge and capabilities
GLO3: Digital literacy

ULO3

 apply segmentation models and its usage

GLO1: Discipline-specific knowledge and capabilities
GLO2: Communication
GLO3: Digital literacy

ULO4

demonstrate practical experience in critically exploring real world business data

GLO2: Communication
GLO3: Digital literacy
GLO4: Critical thinking
GLO5: Problem solving
GLO7: Teamwork

ULO5

evaluate independently on self-directed learning tasks.

GLO6: Self-management

These Unit Learning Outcomes are applicable for all teaching periods throughout the year

Assessment

Assessment Description Student output Weighting (% total mark for unit) Indicative due week
Projects  Written report (3,000-5,000 words), Technical report (3,000-word maximum), 15-minute oral presentation  100% (30%, 50%, 20%) Weeks 7, 9 and 10 or 11

The assessment due weeks provided may change. The Unit Chair will clarify the exact assessment requirements, including the due date, at the start of the teaching period.

Learning Resource

The texts and reading list for the unit can be found on the University Library via the link below: SIT717 Note: Select the relevant trimester reading list. Please note that a future teaching period's reading list may not be available until a month prior to the start of that teaching period so you may wish to use the relevant trimester's prior year reading list as a guide only.

Unit Fee Information

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