units

FIT3002

Faculty of Information Technology

18 September 2017 18 November 2024
Monash University

Undergraduate - Unit

This unit entry is for students who completed this unit in 2014 only. For students planning to study the unit, please refer to the unit indexes in the the current edition of the Handbook. If you have any queries contact the managing faculty for your course or area of study.

print version

6 points, SCA Band 2, 0.125 EFTSL

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered, or view unit timetables.

LevelUndergraduate
FacultyFaculty of Information Technology
OfferedGippsland First semester 2014 (Day)
Gippsland First semester 2014 (Off-campus)
Malaysia First semester 2014 (Day)
South Africa First semester 2014 (Day)
Coordinator(s)Associate Professor Kai Ming Ting (Gippsland); Ms Jojo Wong (Malaysia); Mr Neil Manson (South Africa)

Synopsis

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.

Outcomes

At the completion of this unit students will have -

A knowledge and understanding of:

  • the motivation and the need for data mining;
  • characteristics of major components of the data mining process;
  • the basic principles of methods and operations for data mining;
  • case studies to bridge the connection between hands-on experience and real-world applications;
  • key and emerging application areas;
  • current major research issues.

Developed the skills to:

  • use data mining tools to solve data mining problems.

Assessment

Examination (3 hours): 50%; In-semester assessment: 50%

Chief examiner(s)

Dr Grace Rumantir

Workload requirements

Minimum total expected workload equals 12 hours per week comprising:

(a.) Contact hours for on-campus students:

  • One 2-hour workshop
  • One 2-hour laboratory (for 6 weeks)

(b.) 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.

(c.) Additional requirements (all students):

  • A minimum of 8 hours independent study per week for completing lab and project work, private study and revision.

This unit applies to the following area(s) of study

Business systems

Prerequisites

FIT1004 or FIT2010 or equivalent

Prohibitions

CSE3212, GCO3828

Additional information on this unit is available from the faculty at:

http://www.infotech.monash.edu.au/units/fit3002