Data Mining and its Business Applications - MATH5836

Faculty: Faculty of Science

School: School of Mathematics and Statistics

Course Outline:

Campus: Sydney

Career: Postgraduate

Units of Credit: 6

EFTSL: 0.12500 (more info)

Indicative Contact Hours per Week: 2

CSS Contribution Charge: 2 (more info)

Tuition Fee: See Tuition Fee Schedule

Further Information: See Class Timetable

View course information for previous years.


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.
Science students

Study Levels

UNSW Quick Links