FIT3002 - Applications of data mining - 2018

In the modern corporate world, data is viewed not only as a necessity for day-to-day operation, it is seen as a critical asset for decision making. However, raw data is of low value. Succinct generalisations are required before data gains high value. Data mining produces knowledge from data, making feasible sophisticated data-driven decision making. This unit will provide students with an understanding of the major components of the data mining process, the various methods and operations for data mining, knowledge of the applications and technical aspects of data mining, and an understanding of the major research issues in this area.

On the completion of this unit, students should be able to:

1. explain the motivation of data mining;
2. explain why data mining is needed;
3. explain the characteristics of major components of the data mining process;
4. demonstrate the use of the basic data mining methods;
5. analyse case studies to bridge the connection between hands-on experience and real-world applications;
6. identify key and emerging application areas;
7. use data mining tools to solve data mining problems.

Examination (2 hours plus 30 minutes reading and noting time): 50%; In-semester assessment: 50%

Minimum total expected workload equals 12 hours per week comprising:

1. Contact hours for on-campus students:
   • One 2-hour workshop
   • One 2-hour laboratory (for 6 weeks)
2. Study schedule for off-campus students:
   • Off-campus students generally do not attend lecture and tutorial sessions, however should plan to spend equivalent time working through the relevant resources and participating in discussion groups each week.
3. Additional requirements (all students):
   • A minimum of 8 hours independent study per week for completing lab and project work, private study and revision.

See also Unit timetable information

http://www.infotech.monash.edu.au/units/