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.


Session 1 (30)
On Campus
CSU Study Centre Brisbane
Wagga Wagga Campus
Session 2 (60)
On Campus
CSU Study Centre Brisbane
CSU Study Centre Melbourne
CSU Study Centre Sydney
Term 1 (75)
Wagga 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

Grading System



One session


School of Computing and Mathematics

Enrolment Restrictions

Only available to postgraduate students.

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.


This 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.

Indicative Assessment

The following table summarises the assessment tasks for the online offering of ITC516 in Session 2 2019. Please note this is a guide only. Assessment tasks are regularly updated and can also differ to suit the mode of study (online or on campus).

Item Number
Value %
Online test
Weka and written exercise
Weka data mining
Final exam

The information contained in the CSU Handbook was accurate at the date of publication: October 2020. The University reserves the right to vary the information at any time without notice.