FIT3003 Business intelligence and data warehousing
Faculty of Information Technology
FIT3003 Business intelligence and data warehousing is a level 3, 6-credit-point, undergraduate unit from the Faculty of Information Technology, offered in 2022 in Semester 2 at Clayton and Malaysia. It has no prerequisites.
- Credit points
- 6
- Offered in 2022
- Semester 2
- Clayton, Malaysia
- Assessment
- Exam 60%
- and 3 other tasks
- Workload
- 144 hours
- per semester
This is the 2022 handbook entry. See the 2027 entry.
Reviews
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Requisites
Before FIT3003
No prerequisites or corequisites besides the enrolment rules below.
After FIT3003
No unit lists FIT3003 as a prerequisite in the 2022 handbook.
Enrolment rules
Prerequisites: One of FIT1004, FIT2094 or FIT3171
Overview
Automation and the use of technological tools have resulted in the accumulation of vast volumes of data by modern business organisations. Data warehouses have been set up as repositories to store this data and improved techniques now result in the speedy collection and integration of such data. OLAP technology has resulted in the faster generation of reports and more flexible analysis based on the data repositories. This unit will explore the concepts of data warehousing and OLAP, covering the data processing technological requirements for data warehousing and OLAP and will provide hands on experience on designing data warehousing and OLAP systems.
Offerings in 2022
| Teaching period | Campus | Mode |
|---|---|---|
| Second semester | Clayton | On campus |
| Second semester | Malaysia | On campus |
Assessment
- Take Home TestOtherThreshold hurdle10%
- Major AssignmentAssignmentThreshold hurdle25%
- FLUX ParticipationOtherThreshold hurdle5%
- Scheduled final assessment (2 hours and 10 minutes)ExamThreshold hurdle60%
Learning outcomes
When you finish this unit, you should be able to:
- 1
Design multi-dimensional databases and data warehouses;
- 2
Use fact and dimensional modelling;
- 3
Implement online analytical processing (OLAP) queries;
- 4
Explain the roles of data warehousing architecture and the concepts of granularity in data warehousing;
- 5
Create business intelligence reports using data warehouses and OLAP.
Workload and teaching
- Lectures24 hours
- Laboratories24 hours
- Teaching approachActive learning
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled online and face to face learning activities and independent study. Independent study may include associated reading and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning and online engagement.
Learning resources
Technology resources
ORACLE DBMS for applying the ETL process and building a star schema.
Where it fits
FIT3003 is part of 7 areas of study in the 2022 handbook.
- BUSANLMJ01Additional business analytics unitsBusiness analyticsNo reviews yet
- BUSANLYT05Additional business analytics unitsBusiness analyticsNo reviews yet
- ITBIS01Level 3 unitsBusiness information systemsNo reviews yet
- COMPUSC05Sequence 2Computational scienceNo reviews yet
- COMPUSC07Additional computational science unitComputational scienceNo reviews yet
- DATASCI01Level 3 elective unitsData scienceNo reviews yet
- SFTWRENG01Software engineering technical electivesSoftware engineeringNo reviews yet
Contacts
- Chief Examiners
- Associate Professor David Taniar
- Unit Coordinators
- Dr Soon Lay Ki
- Agnes Haryanto
Common questions
What are the prerequisites for FIT3003?
FIT3003 has no prerequisites, but enrolment rules apply.
When is FIT3003 offered?
In 2022, FIT3003 runs in Semester 2 at Clayton and Malaysia.
How much work is FIT3003?
The handbook expects about 144 hours of study across the semester. No students have rated its difficulty yet.
Does FIT3003 have an exam?
Yes. The exam is worth 60% of the final mark, alongside 3 other tasks.
Which majors and minors include FIT3003?
FIT3003 is part of Business analytics, Business information systems, Computational science, Data science and Software engineering.