Course Title: Data Mining

Part A: Course Overview

Course Title: Data Mining

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

COSC2110

City Campus

Undergraduate

140H Computer Science & Information Technology

Face-to-Face

 Sem 1 2006,
Sem 1 2007,
Sem 1 2010,
Sem 1 2011,
Sem 1 2012,
Sem 1 2013,
Sem 1 2014,
Sem 1 2015

COSC2110

City Campus

Undergraduate

171H School of Science

Face-to-Face

 Sem 2 2017,
Sem 2 2019,
Sem 2 2020,
Sem 2 2021

COSC2110

City Campus

Undergraduate

175H Computing Technologies

Face-to-Face

 Sem 2 2022,
Sem 2 2024

COSC2111

City Campus

Postgraduate

140H Computer Science & Information Technology

Face-to-Face

 Sem 1 2006,
Sem 1 2007,
Sem 1 2010,
Sem 1 2011,
Sem 1 2012,
Sem 1 2013,
Sem 1 2014,
Sem 1 2015

COSC2111

City Campus

Postgraduate

171H School of Science

Face-to-Face

 Sem 2 2017,
Sem 2 2018,
Sem 2 2019,
Sem 2 2020,
Sem 2 2021

COSC2111

City Campus

Postgraduate

175H Computing Technologies

Face-to-Face

 Sem 2 2022,
Sem 2 2024

COSC2989

RMIT University Vietnam

Postgraduate

175H Computing Technologies

Face-to-Face

 Viet2 2023,
Viet1 2024

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.

For information go to RMIT Course Requisites webpage.

 

Recommended Prior Study

It is recommended to have satisfactorily completed the following course/s before you commence this course:

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.    

If you are enrolled in this course as a component of your Bachelor Honours Program, your overall mark will contribute to the calculation of the Weighted Average Mark (WAM).

See the WAM information web page for more information.


 


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 should 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

Key concepts will be explained in pre-recorded lecture videos, where syllabus material will be presented and the subject matter will be illustrated with demonstrations and examples.

Tutorials, workshops and/or labs and/or group discussions (including online forums):  These will focus on problem solving and projects. They will provide practice in the application of theoretical concepts and algorithms, allow exploration of concepts with teaching staff and other students and give feedback on your progress and understanding.

Assignments: Assignments will require an integrated understanding and application of the concepts and algorithms.
Private study: This involves working through the course material as presented in the classes.    


Overview of Learning Resources

The course is supported by the Canvas learning management system which provides specific learning resources. See the RMIT Library Guide at http://rmit.libguides.com/compsci     

 


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
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.