• 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, Information-Intensive Computing, Algorithms and Data, Computational Foundations
  • Academic career UGRD
  • Course convener
    • Dr Kerry Taylor
  • Mode of delivery In Person
  • Co-taught Course
  • Offered in First Semester 2018
    See Future Offerings

Massive amounts of data are being collected by public and private organisations, and research projects, while the Internet provides a very large source of information about almost every aspect of human life and society. Analysing such data can provide significant benefits to an organisation. 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:

Upon successful completion of this course, students will:
  1. Critically analyse and justify the steps involved in the data mining process.
  2. Anticipate and identify data issues related to data mining.
  3. 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. Reflect upon ethical and social impacts of data mining.

Indicative Assessment

  1. Written and practical assignments (30%)
  2. Oral presentation and report (20%)
  3. Final examination (50%)

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

The workload for the course is around 130 hours, including reading, the viewing of online course material, participation in face-to-face lectures, practical labs and tutorials, and preparation for assessments.

Requisite and Incompatibility

To enrol in this course you must have completed 6 units from COMP1100 or COMP1130 or COMP1730; and COMP2400. You are not able to enrol in this course if you have previously completed COMP3420 or COMP8400.

Prescribed Texts

  • Han, Kamber and Pei: Data Mining – Concepts and Techniques, 3rd Edition, 2011

Preliminary Reading

A further set of readings will be provided at the start of the course

Majors

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
2018 $4080
International fee paying students
Year Fee
2018 $5400
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
4844 19 Feb 2018 27 Feb 2018 31 Mar 2018 25 May 2018 In Person N/A

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