Use the Tab and Up, Down arrow keys to select menu items.
Parametric and non-parametric statistical methodologies and algorithms for data mining.
STAT318 is a course in statistical learning and data mining, suited to anyone with an interest in analysing large datasets. This course will introduce a variety of statistical learning and data mining techniques for classification, regression, clustering and association purposes. Possible topics include, classification and regression trees, random forests, Apriori algorithm, FP-growth algorithm and support vector machines. The lectures will be supplemented with laboratory sessions using the statistical software package R.
The courses will:introduce statistical learning and data miningintroduce advanced data analysis techniques for classification, regression, cluster analysis and association analysisintroduce the use of the statistics software package RYou will be able to:describe and conduct appropriate statistical modeling techniquesbe able to interpret the analysis results in such a way that a non-user of statistics can understandUse R competentlyWrite a scientific and technical report
15 points from MATH102, EMTH118 or MATH199; and another 30 points from 200 level STAT, COSC, DATA, MATH or EMTH
Students must attend one activity from each section.
Domestic fee $847.00
International fee $4,988.00
* All fees are inclusive of NZ GST or any equivalent overseas tax, and do not include any programme level discount or additional course-related expenses.
For further information see
Mathematics and Statistics