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Overview

Increasingly, organisations need to analyse enormous data sets to determine useful structure in them. In response to this, a range of statistical methods and tools have been developed in recent times to allow accurate and quick analysis of these sets.

Topics include: choosing the right data mining tool for your data, linear methods (logistic regression and generalized linear models) and data mining, clustering methods, decision trees, multivariate adaptive regression splines, wavelet smoothing, hybrid models, neural networks, support vector machines, bagging and boosting methods. Case studies of industry-based data mining projects feature prominently. The most recent data mining software is used to illustrate the methods.

Study Level

Postgraduate

Offering Terms

Term 3

Campus

Kensington

Delivery Mode

Fully on-site

Indicative contact hours

4

Course Outline

To access course outline, please visit:

Fees

Pre-2019 Handbook Editions

Access past handbook editions (2018 and prior)

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