• Offered by Research School of Computer Science
  • ANU College ANU College of Engineering and Computer Science
  • Course subject Computer Science
  • Areas of interest Computer Science, Information Technology, Software Engineering
  • Academic career UGRD
  • Course convener
    • Dr Peter Christen
  • Mode of delivery In Person
  • Offered in First Semester 2015
    See Future Offerings

This course examines the design of databases and data warehouses and their use for data mining; and investigates associated issues. Topics may include: relational theory and conceptual modelling; privacy and security; statistical databases; distributed databases; data warehousing; data cleaning and integration; and data mining concepts and techniques.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

On completion of this course, the students should have gained a good understanding of basic concepts, principles and techniques in data warehousing and data mining. Specifically, the students are able to perform the following tasks.

  • Understand fundamental concepts of data warehousing and OLAP techniques
  • Apply data-cubing techniques and conduct multi-dimensional data analysis
  • Demonstrate advanced knowledge on the design and implementation of data warehouses

  • Develop in-depth understanding of fundamental data mining algorithms

  • Apply data mining techniques for knowledge discovery

  • Perform practical data mining using open source tools

Indicative Assessment

  • Two assignments (40 marks)
  • Online quizzes (5 marks)
  • Final Exam (55 marks)

The ANU uses Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.

Workload

Thirty one-hour lectures and six two-hour tutorials

Requisite and Incompatibility

To enrol in this course you must have completed COMP2400 and 6 units of 1000 level COMP courses and 6 units of 2000 level COMP courses; and 6 units of 1000 level MATH courses or 1000 level STAT courses.

Prescribed Texts

The following text book will be used for this course:

  • Jiawei Han, Micheline Kamber, and Jian Pei,Data Mining:Concepts and Techniques, 3rd edition, Morgan Kaufmann, 2011 http://www.cs.uiuc.edu/~hanj/bk3/

Majors

Minors

Fees

Tuition fees are for the academic year indicated at the top of the page.  

If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.

Student Contribution Band:
2
Unit value:
6 units

If you are an undergraduate student and have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). You can find your student contribution amount for each course at Fees.  Where there is a unit range displayed for this course, not all unit options below may be available.

Units EFTSL
6.00 0.12500
Domestic fee paying students
Year Fee
2015 $3096
International fee paying students
Year Fee
2015 $4146
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

The list of offerings for future years is indicative only.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.

First Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
1918 16 Feb 2015 06 Mar 2015 31 Mar 2015 29 May 2015 In Person N/A

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