• Offered by School of Computing
  • ANU College ANU College of Engineering Computing & Cybernetics
  • Classification Advanced
  • Course subject Computer Science
  • Areas of interest Business Information Systems, Computer Science, Mathematics, Algorithms and Data, Artifical Intelligence
  • Academic career PGRD
  • Mode of delivery In Person

Massive amounts of data are collected by public and private organisations, while the Web offers rich information about almost every aspect of human life and society. Analysing such data can provide significant benefits to an organisation or research project. This course provides a practical focus on the technology and research in the area of data mining. It focuses on the algorithms and techniques and less on the mathematical and statistical foundations.

Learning Outcomes

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

  1. Critically analyse and justify the steps involved in the data mining process
  2. Anticipate and identify data issues related to data mining
  3. Research, test and apply the principal algorithms and techniques used in data mining
  4. Justify suitable techniques to use for a given data mining problem
  5. Appraise and reflect upon the results of a data mining project using suitable measurements
  6. Investigate application areas and current research directions of data mining
  7. Reflect upon ethical and social impacts of data mining

Other Information

You should be enrolled in the Master of Applied Data Analytics, the Graduate Diploma of Applied Data Analytics, or the Graduate Certificate of Data Engineering to undertake this blended intensive course.

Note: Non-MADAN, DADAN or GCDE students wanting to enrol are required to seek approval from their Program Convener.

Indicative Assessment

  1. Regular formative quizzes (5) [LO 1,2,3,4,5,6,7]
  2. Written assignment: essay (15) [LO 1,6,7]
  3. Mid-course lab exam (20) [LO 1,2,3,4,5]
  4. Practical assignment: report (20) [LO 1,2,3,4,5]
  5. Final examination (40) [LO 1,2,3,4,5,6,7]

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.


Approx 130 hours

Inherent Requirements


Requisite and Incompatibility

To enrol in this course you must have completed COMP7240 or COMP6240 or COMP2400; and COMP6730 or COMP7230 or COMP6710; and be enrolled in the Master of Applied Data Analytics or the Graduate Diploma of Applied Data Analytics or the Graduate Certificate of Data Engineering. Incompatible with COMP3420 and COMP3425 and COMP8400 and COMP8410 .

Prescribed Texts


Assumed Knowledge

Mathematics, studied at Senior Secondary level


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

Commonwealth Support (CSP) Students
If you 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). More information about your student contribution amount for each course at Fees

Student Contribution Band:
Unit value:
6 units

If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.

Where there is a unit range displayed for this course, not all unit options below may be available.

6.00 0.12500
Domestic fee paying students
Year Fee
2024 $4980
International fee paying students
Year Fee
2024 $6360
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

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There are no current offerings for this course.

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