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Parametric and non-parametric statistical methodologies and algorithms for data mining.
STAT318 and STAT462 are courses in Data Mining, suited to anyone with an interest in analysing data. In these courses we introduce you to the statistical analysis of large datasets for both classification and association purposes. We cover analysis of both numeric and qualitative data and make use of the professional software package MATLAB.In these courses we will show you how to use the package, enter, manipulate and analyze data in MATLAB.The Courses will:• introduce data mining.• introduce advanced data analysis techniques including classification and regression trees, ROC curves and FP-growth algorithm.• introduce the use of the statistics computer package MATLAB.
describe and conduct appropriate statistical modeling techniques for large datasets be able to interpret the model results in such a way that a non-user of statistics can understand use MATLAB competently write a scientific and technical report
This course will provide students with an opportunity to develop the Graduate Attributes specified below:
Critically competent in a core academic discipline of their award
Students know and can critically evaluate and, where applicable, apply this knowledge to topics/issues within their majoring subject.
i) 15 points from STAT200 to STAT299 and ii) a further 15 points from STAT200 to STAT299 or COSC200-299 or any other relevant subject with Head of School approval.
Assignments give you practice in analysing data and presenting results in a written report.The project will give the opportunity to acquire presentation skills.The lectures are complemented by computer labs where you will be guided in conducting approriate analysis and modelling.
TextbookRecommended reading:Tan, Steinbach and Kumar 2006. Introduction to Data Mining. 769pp.This is on a restricted loan in the Library.
Domestic fee $622.00
International fee $3,200.00
* Fees include New Zealand GST and do not include any programme level discount or additional course related expenses.
For further information see
Mathematics and Statistics.