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 |
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 |
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
COSC2531 Programming Fundamentals
OR
COSC1076 Advanced Programming Techniques
OR
COSC1284 Programming Techniques
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
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 and not a core course, so that it does not need to contribute to the Program Learning Outcomes.
Course Learning Outcomes
Upon successful completion of this course you should be able to:
- Demonstrate advanced knowledge of data mining concepts and techniques
- Apply the techniques of clustering, classification, association finding, feature selection and visualisation on real world data
- Determine whether a real world problem has a data mining solution
- Apply data mining software and toolkits in a range of applications
- Set up a data mining process for an application, including data preparation, modelling and evaluation
- Demonstrate knowledge of the ethical considerations involved in data mining.
Please note that postgraduate students are expected to demonstrate deeper knowledge and higher level application of knowledge and skills than undergraduate students.
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.
A total of 120 hours of study is expected during this course, comprising:
Teacher-directed hours (36 hours): lectorials and laboratory sessions. Each week there will be a 2-hour lectorial, and a 1-hour lab session. You are encouraged to participate during lectorials through asking questions, commenting on the lecture material based on your own experience and by presenting solutions to written exercises. The lectorial / laboratory sessions will introduce you to the tools necessary to undertake the assignment work.
Student-directed hours (84 hours): You are expected work independently outside class on understanding the lecture material, solving tutorial problems and completing assignments.
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
The assessment for this course comprises written assignments. The assignments involve implementation of data mining concepts and techniques and may involve a class presentation.
Note: This course has no hurdle requirements.
Assessment tasks
Assignment 1:
Weighting 40%
This assessment task supports CLOs 1, 2, 4, 5, 6
Assignment 2:
Weighting 40%
This assessment task supports CLOs 1, 2, 4, 5, 6
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
Please note that postgraduate students are expected to demonstrate deeper knowledge and higher-level application of knowledge and skills than undergraduate students.