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CP5634 - Data Mining

Credit points: 3
Year: 2022
Student Contribution Band: Band 2
Administered by: College of Science and Engineering

This subject focuses on advanced data mining techniques for intelligence informatics. It provides an in-depth coverage of the need for big data analysis, data warehousing, big data 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 intelligent informatics solutions.

Learning Outcomes

  • 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;
  • assess the potential benefits, risks, issues and challenges associated with big data and data mining;
  • explore and analyse data mining patterns for intelligence informatics.

Subject Assessment

  • Written > Examination (centrally administered) - (40%) - Individual
  • Written > Test/Quiz 1 - (20%) - Individual
  • Written > Project report - (40%) - Group.
Prerequisites: CP5633 OR ADMISSION TO MIT-MBA (112404)
Inadmissible
Subject
Combinations:
CP3300 CP3403 CP5605

Availabilities

Cairns, Study Period 1, Internal
Census Date 24-Mar-2022
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 26 hours lectures (didactic or interactive)
  • 12 hours practicals
  • 6 hours workshops
  • assessment and self-directed study

JCU Brisbane, Trimester 1, Internal
Census Date 10-Mar-2022
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 26 hours lectures (didactic or interactive)
  • 12 hours practicals
  • 6 hours workshops
  • assessment and self-directed study

JCU Brisbane, Trimester 2, Internal
Census Date 23-Jun-2022
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 26 hours lectures (didactic or interactive)
  • 12 hours practicals
  • 6 hours workshops
  • assessment and self-directed study

JCU Brisbane, Trimester 3, Internal
Census Date 06-Oct-2022
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 26 hours lectures (didactic or interactive)
  • 12 hours practicals
  • 6 hours workshops
  • assessment and self-directed study

JCU Singapore, Study Period 51, Internal
Census Date 07-Apr-2022
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 44 hours - Combined Lectures; Tutorials/Workshops/ Practicals; Lecturer directed activities
  • assessment and self-directed study

JCU Singapore, Study Period 52, Internal
Census Date 04-Aug-2022
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 44 hours - Combined Lectures; Tutorials/Workshops/ Practicals; Lecturer directed activities
  • assessment and self-directed study

JCU Singapore, Study Period 53, Internal
Census Date 01-Dec-2022
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 44 hours - Combined Lectures; Tutorials/Workshops/ Practicals; Lecturer directed activities
  • assessment and self-directed study

Townsville, Study Period 1, Internal
Census Date 24-Mar-2022
Workload expectations:

The student workload for this 3 credit point subject is approximately 130 hours.

  • 12 hours practicals
  • 6 hours workshops
  • 26 hours online interactive lectures
  • 12 hours online collaboration sessions
  • assessment and self-directed study

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.