ITC516 Data Mining and Visualisation for Business Intelligence (8)


This subject provides an in-depth coverage of the principles of data mining and the application of visual datasets for business intelligence. It focuses on the need for developing timely and accurate views of large datasets and the need for the creation of visual displays, or dashboards, to present accurate views of complex data trends and patterns. Students who undertake this subject are expected to have a basic working knowledge of database theory and operations.

+ Subject Availability Modes and Location

Session 2
InternalCSU Study Centre Melbourne
InternalCSU Study Centre Sydney
Session 3
DistanceWagga Wagga Campus
Continuing students should consult the SAL for current offering details: ITC516
Where differences exist between the Handbook and the SAL, the SAL should be taken as containing the correct subject offering details.

Subject information

Duration Grading System School:
One sessionHD/FLSchool of Computing and Mathematics

Enrolment restrictions

Available only to students enrolled in:
Master of Systems Development;
Master of Information Systems Security;
Graduate Certificate in Systems Development;
Graduate Certificate in Systems Security.

Learning Outcomes

Upon successful completion of this subject, students should:
  • be able to identify and analyse business requirements for the identification of patterns and trends in data sets;
  • be able to appraise the different approaches and categories of data mining problems;
  • be able to compare and evaluate output patterns;
  • be able to explore and critically analyse data sets and evaluate their data quality, integrity and security requirements;
  • be able to compare and evaluate appropriate techniques for detecting and evaluating patterns in a given data set;
  • be able to identify and evaluate the security, privacy and ethical implications in data mining;
  • be able to explain the importance of current and future trends likely to affect data mining and visualisation.


The subject will cover the following topics:
  • Principles of data mining.
  • Data mining techniques to identify trends and patterns.
  • Data visualisation principles and techniques.
  • Security, privacy and ethical implications involved in data mining and predictive analysis.
  • Issues and trends in unstructured data, data mining and visualisation.


The information contained in the 2016 CSU Handbook was accurate at the date of publication: 06 September 2016. The University reserves the right to vary the information at any time without notice.