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Data Mining
This occurrence of the course is for online students only. On-campus students should enrol in the (C) occurrence of this course.STAT462 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
This course will provide students with an opportunity to develop the Graduate Attributes specified below:
Employable, innovative and enterprising
Students will develop key skills and attributes sought by employers that can be used in a range of applications.
Subject to approval of the Head of School.
Students must attend one activity from each section.
Philipp Wacker
Heyang (Thomas) Li
Hastie, Trevor. , Tibshirani, Robert., Friedman, J. H; The elements of statistical learning : data mining, inference, and prediction ; 2nd ed; Springer, 2009.
James, Gareth; An introduction to statistical learning : with applications in R ; Springer, 2013.
Domestic fee $1,074.00
International Postgraduate fees
* 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 .