Part A: Course Overview

Course Title: Data Mining

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

COSC3126

RMIT University Vietnam

Postgraduate

175H Computing Technologies

Face-to-Face

 Viet1 2025

Course Coordinator: Xiaodong Li

Course Coordinator Phone: +61 3 9925 9585

Course Coordinator Email: xiaodong.li@rmit.edu.au

Course Coordinator Location: 14.08.14A

Course Coordinator Availability: by appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

Enforced Pre-Requisite Courses
Successful completion of the following course/s:

Note: it is a condition of enrolment at RMIT that you accept responsibility for ensuring that you have completed the prerequisite/s and agree to concurrently enrol in co-requisite courses before enrolling in a course.

Further information on RMIT Course Requisites policy can be found at: https://www.rmit.edu.au/students/student-essentials/enrolment/course-requisites

 

If you have completed prior studies at RMIT or another institution that developed the skills and knowledge covered in the above course/s you may be eligible to apply for credit transfer.

Alternatively, if you have prior relevant work experience that developed the skills and knowledge covered in the above course/s you may be eligible for recognition of prior learning.

Please follow the link for further information on how to apply for credit for prior study or experience.


Course Description

This course is concerned with data mining - that is, finding interesting and useful patterns in large data repositories. It aims to provide you with up-to-date conceptual and practical knowledge on recent developments in data mining. At the end of this course, you will understand the main concepts, principles and techniques of data mining. For practical work you will be using a popular data mining package to analyse data of various forms, including transaction data, relational data and textual data.    


 


Objectives/Learning Outcomes/Capability Development

Program Learning Outcomes

This course is an option course so it is not required to contribute to the development of program learning outcomes (PLOs) though it may assist your achievement of several PLOs.

For more information on the program learning outcomes for your program, please see the program guide.


Upon successful completion of this course, you will be able to:

  1. Demonstrate advanced knowledge of data mining concepts and techniques
  2. Apply the techniques of clustering, classification, association finding, feature selection and visualisation on real world data
  3. Determine whether a real world problem has a data mining solution
  4. Apply data mining software and toolkits in a range of applications  
  5. Set up a data mining process for an application, including data preparation, modelling and evaluation
  6. Demonstrate knowledge of the ethical considerations involved in data mining.


Overview of Learning Activities

You will be actively engaged in a range of learning activities such as lectorials, tutorials, practicals, laboratories, seminars, project work, class discussion, individual and group activities. Delivery may be face to face, online or a mix of both.

You are encouraged to be proactive and self-directed in your learning, asking questions of your lecturer and/or peers and seeking out information as required, especially from the numerous sources available through the RMIT library, and through links and material specific to this course that is available through myRMIT Studies Course.


Overview of Learning Resources

RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course.

There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal.


Overview of Assessment

Note: This course has no hurdle requirements.

Assessment tasks

Assessment task 1: Assignment 1
Weighting 40%
This assessment task supports CLOs 1, 2, 4, 5, 6

Assessment task 2: Assignment 2
Weighting 40%
This assessment task supports CLOs 1, 2, 4, 5, 6

Assessment task 3: End-of-semester Test: (online, time-limited test within a 24-hour time window)
Weighting 20% 
This assessment supports CLOs 1, 2, 3, 4, 5, 6

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.