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Parametric and non-parametric statistical methodologies and algorithms for data mining.
STAT318 and STAT462 are courses in statistical learning and data mining, suited to anyone with an interest in analysing large datasets. The courses 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 $824.00
International fee $4,750.00
* 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