6 points, SCA Band 3, 0.125 EFTSL
Undergraduate - Unit
Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.
- First semester 2018 (On-campus)
Students must have completed 12 credit points in any second year level unit in any discipline.
, CSE3212, GCO3828 or equivalent.
This unit aims to provide an understanding and application of the tools and techniques of data mining in delivering superior value added propositions to businesses. Students will learn the data mining methodology, appropriate techniques to apply in different cases, practical use of data mining software and how to interpret the knowledge generated from these tools. Students will be exposed to emerging areas in data mining, such as applications of data mining in the cloud.
Students will also learn about ethical concerns on the use of data mining. Superior data mining skills and knowledge enables the business to maximise the value of current customers, through creative and critical analysis of favourable circumstances and possibilities for gaining increasing business and or reducing costs from current customers.
The learning goals associated with this unit are to:
- appreciate the different stages of the data mining process
- understand the role that data mining play in various areas of business
- select the appropriate data mining tools to suit the problem at hand
- have hands-on experience on using data mining tools on various case studies such as direct marketing campaigns and product introductions, and analysing customer churn
- interpret and apply critical thinking on the results generated from the data mining models.
Within semester assessment: 50% + Examination: 50%
Minimum total expected workload to achieve the learning outcomes for this unit is 144 hours per semester typically comprising a mixture of scheduled learning activities and independent study. Independent study may include associated readings, assessment and preparation for scheduled activities. The unit requires on average three/four hours of scheduled activities per week. Scheduled activities may include a combination of teacher directed learning, peer directed learning and online engagement.
See also Unit timetable information