Course has been adjusted for remote participation in S1 2021 Some on-campus activities are available. Attendance at these where possible is encouraged
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:- Critically analyse and justify the steps involved in the data mining process.
- Anticipate and identify data issues related to data mining.
- Test and apply the principal algorithms and techniques used in data mining.
- Justify suitable techniques to use for a given data mining problem.
- Appraise and reflect upon the results of a data mining project using suitable measurements.
- Reflect upon ethical and social impacts of data mining.
Indicative Assessment
- Written and practical assignments (30%)
- Oral presentation and report (20%)
- 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
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.
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:
- 2
- 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.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2021 | $4410 |
- International fee paying students
| Year | Fee |
|---|---|
| 2021 | $5880 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
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 |
|---|---|---|---|---|---|---|
| 3052 | 22 Feb 2021 | 01 Mar 2021 | 31 Mar 2021 | 28 May 2021 | In Person | N/A |
