Note: This is an archived Handbook entry from 2015.
|Dates & Locations:|| |
This subject has the following teaching availabilities in 2015:Semester 1, Parkville - Taught on campus.
Timetable can be viewed here. For information about these dates, click here.
|Time Commitment:||Contact Hours: 36 hours over the semester. |
Total Time Commitment:
|Recommended Background Knowledge:|| |
|Non Allowed Subjects:|| |
|Core Participation Requirements:||
For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.
It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability
CoordinatorDr Sean Maynard
Data warehouses are designed to provide organizations with an integrated set of high quality data to support decision-makers. They should support flexible and multi-dimensional retrieval and analysis of data. Topics covered include data warehousing and decision-making, data warehouse design, data warehouse implementation, data sourcing and data quality, on-line analytical processing (OLAP) and data mining, customer relationship management systems, and case studies of data warehousing practice. This subject is part of the Business Analytics stream within the Master of Information Systems.
This subject introduces the compelling need for data warehousing, data warehouse architectures, decision making, data warehouse design, data warehouse modelling, data quality, data warehouse implementation - including the Extract Transform Load (ETL) process, and data warehouse use in supporting decision making – including decision making tools and OLAP. Readings are provided for all topics that introduce real world cases on data warehousing and related areas and include the use of data warehousing for competitive advantage, success and failure stories in Data Warehousing.
INTENDED LEARNING OUTCOMES (ILOs)
Having completed this subject the student is expected to:
Hurdle requirement: To pass the subject students must obtain at least:
|Breadth Options:|| |
This subject is not available as a breadth subject.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
On completion of this subject students should have the following skills:
LEARNING AND TEACHING METHODS
The subject is delivered in 3 hour classes. Each class will be made up of a combination of lectures, discussions and tutorial type activities. Outside class students will study the various aspects of data warehousing through prescribed readings.
INDICATIVE KEY LEARNING RESOURCES
All required readings are available via the LMS.
CAREERS / INDUSTRY LINKS
This subject is relevant to careers in data warehousing, data analysis, data mining, and information management. A guest lecturer will present at least one week’s worth of materials about data warehousing in industry.
Master of Information Systems |
Master of Information Systems
Master of Information Systems
Master of Operations Research and Management Science
Master of Philosophy - Engineering
Master of Science (Information Systems)
MIS Professional Specialisation |
MIS Research Specialisation
MIT Health Specialisation
MIT Spatial Specialisation
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