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CP3403 - Data Mining

Credit points: 3
Year: 2020
Student Contribution Band: Band 2
Administered by: College of Science and Engineering

Students will be introduced in this subject to fundamental data mining and big data concepts, technologies, and applications. Topics include data mining, data warehousing, big data analytics, data visualisation, pre-processing, clustering, classification and association rules mining. Students will gain hands-on experience with popular data mining tools and develop skills in practical data mining and big data analytics.

Learning Outcomes

  • explain the notion of big data, information intelligence, data mining, data profiling, data cleaning and data integration and warehousing;
  • analyse and apply data mining techniques and tools;
  • examine the working principles of data mining and knowledge discovery in databases;
  • relate intelligence informatics to data warehousing, big data and data mining;
  • assess the potential benefits, risks, issues and challenges associated with big data and data mining.

Subject Assessment

  • end of semester exam (40%)
  • assignment 1 (30%)
  • practicals (20%)
  • presentation (10%).
Prerequisites: 6 CP OF CP SUBJECTS AND 12 CP OF SUBJECTS
Inadmissible
Subject
Combinations:
CP3300 CP5634

Availabilities

Townsville, Internal, Study Period 1
Census Date 26-Mar-2020
Coord/Lect: Professor Ickjai Lee.
Workload expectations:

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

  • 38 hours - Combined Lectures; Practicals; Lecturer directed activities.
  • assessment and self-directed study

External, Study Period 1
Census Date 26-Mar-2020
Coordinator: Professor Ickjai Lee
Workload expectations:

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

  • 0 hours
  • assessment and self-directed study

Cairns, Internal, Study Period 1
Census Date 26-Mar-2020
Coord/Lect: Professor Ickjai Lee.
Workload expectations:

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

  • 38 hours - Combined Lectures; Practicals; Lecturer directed activities.
  • assessment and self-directed study

JCU Singapore, Internal, Study Period 51
Census Date 09-Apr-2020
Workload expectations:

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

  • 38 hours - Combined Lectures; Practicals; Lecturer directed activities.
  • assessment and self-directed study

JCU Singapore, Internal, Study Period 53
Census Date 03-Dec-2020
Workload expectations:

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

  • 38 hours - Combined Lectures; Practicals; Lecturer directed activities.
  • 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.