Use the Tab and Up, Down arrow keys to select menu items.
Data Mining
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.
Biculturally competent and confident
Students will be aware of and understand the nature of biculturalism in Aotearoa New Zealand, and its relevance to their area of study and/or their degree.
Globally aware
Students will comprehend the influence of global conditions on their discipline and will be competent in engaging with global and multi-cultural contexts.
Subject to approval of the Head of School.
Students must attend one activity from each section.
Heyang (Thomas) Li
Philipp Wacker
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.
G. James, D. Witten, T. Hastie and R. Tibshirani, An Introduction to Statistical Learning with Applications in R. (2014) SpringerRecommended reading:T. Hastie, R. Tibshirani and J. Friedman, The Elements of Statistical Learning, Data Mining, Inference, and Prediction. (2013) Springer.
School of Mathematics and Statistics Postgraduate Handbook General information for students Library portal LEARN
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 .