The subject provides students with in-depth study of data and knowledge engineering and their use in real life business. It looks into interpreting data through advanced approaches such as an ensemble of trees and clustering. Given the importance of clean and useful data for knowledge discovery, it offers thorough discussion on data pre-processing tasks including missing value imputation, corrupt data detection, discretization, and feature selection. The subject offers a study of the preservation of privacy when data mining, publishing and sharing among business organisations. It uses the current tools for knowledge discovery and future prediction.
School of Computing and Mathematics
Only available to postgraduate students.
Familiarity with data mining and visualisation concepts similar to the levels covered in ITC516 and ITC556
The following table summarises the assessment tasks for the online offering of ITC573 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).
The information contained in the CSU Handbook was accurate at the date of publication: August 2020. The University reserves the right to vary the information at any time without notice.