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CP5634 - Business Intelligence and Data Mining

Credit points: 3
Year: 2015
Student Contribution Band: Band 2
Administered by: College of Business, Law & Governance

This subject focuses on advanced data mining techniques for business intelligence. It provides an in-depth coverage of the need for big data analysis, data warehousing, business analytics, predictive methods, scalability considerations, data visualisation, and data mining techniques. Students will gain hands-on experience with various data mining tools and embedding data mining in business solutions.

Learning Outcomes

  • assess the potential benefits, risks, issues and challenges associated with business intelligence and data mining;
  • explain the importance of big data analysis and data mining;
  • identify and critically evaluate data mining techniques and tools;
  • compare and evaluate appropriate techniques for clustering, classification and association rules mining;
  • explore and analyse data mining patterns for business intelligence.
Inadmissible
Subject
Combinations:
CP3300 CP3403 CP5605

Availabilities

Townsville, Internal, Study Period 2
Census Date 27-Aug-2015
Coord/Lect: Dr Kyungmi Joanne Lee.
Workload expectations:
  • 26 hours lectures
  • 12 hours practicals
  • 6 hours workshops/Seminars
Assessment: end of semester exam (40%); other exams (20%); data mining practice (40%).

Townsville, Internal, Study Period 1
Census Date 26-Mar-2015
Coordinator: Professor Ickjai Lee
Lecturer: Assoc. Professor Hossein Ghodosi.
Workload expectations:
  • 26 hours lectures
  • 12 hours practicals
  • 6 hours workshops/Seminars
Assessment: end of semester exam (40%); other exams (20%); data mining practice (40%).

Cairns, Internal, Study Period 2
Census Date 27-Aug-2015
Coord/Lect: Dr Kyungmi Joanne Lee.
Workload expectations:
  • 26 hours lectures
  • 12 hours practicals
  • 6 hours workshops/Seminars
Assessment: end of semester exam (40%); other exams (20%); data mining practice (40%).

Cairns, Internal, Study Period 1
Census Date 26-Mar-2015
Coordinator: Professor Ickjai Lee
Lecturer: Assoc. Professor Hossein Ghodosi.
Workload expectations:
  • 26 hours lectures
  • 12 hours practicals
  • 6 hours workshops/Seminars
Assessment: end of semester exam (40%); other exams (20%); data mining practice (40%).

JCU Singapore, Internal, Study Period 52
Census Date 30-Jul-2015
Workload expectations:
  • 26 hours lectures
  • 12 hours practicals
  • 6 hours workshops/Seminars
Assessment: end of semester exam (40%); other exams (20%); data mining practice (40%).
Restrictions: An enrolment quota applies to this offering.

JCU Singapore, Internal, Study Period 53
Census Date 26-Nov-2015
Workload expectations:
  • 26 hours lectures
  • 12 hours practicals
  • 6 hours workshops/Seminars
Assessment: end of semester exam (40%); other exams (20%); data mining practice (40%).

JCU Brisbane, Internal, Study Period 22
Census Date 13-Aug-2015
Coordinator: Mr Ian Kirkwood
Workload expectations:
  • 26 hours lectures
  • 12 hours practicals
  • 6 hours workshops/Seminars
Assessment: end of semester exam (40%); other exams (20%); data mining practice (40%).

JCU Brisbane, Internal, Study Period 23
Census Date 10-Dec-2015
Coordinator: Mr Ian Kirkwood
Workload expectations:
  • 26 hours lectures
  • 12 hours practicals
  • 6 hours workshops/Seminars
Assessment: end of semester exam (40%); other exams (20%); data mining practice (40%).

JCU Brisbane, Internal, Study Period 21
Census Date 02-Apr-2015
Coordinator: Mr Ian Kirkwood
Workload expectations:
  • 26 hours lectures
  • 12 hours practicals
  • 6 hours workshops/Seminars
Assessment: end of semester exam (40%); other exams (20%); data mining practice (40%).

Note: Minor variations might occur due to the continuous Subject quality improvement process, and in case of minor variation(s) in assessment details, the Subject Outline represents the latest official information.