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A practical approach to data mining with large volumes of complex data; prepare, cleanse and explore data; supervised and unsupervised modelling with association rules and market basket analysis, decision trees, multi-layer neural networks, k-nearest neighbours, k-means clustering and self-organising maps, ensemble and bundling techniques, text mining; use of leading software tools; business examples and research literature.
Note(s): Access to a Windows PC is required for analysis of data.
Note: You may enrol in a postgraduate course (that is a 700-, 800- or 900-level course) if you meet the prerequisites for that course and have been admitted to a qualification which lists the course in its schedule.
|2018||Semester One full semester||Block||Auckland Campus|
|2018||Semester One full semester||Block||Wellington Campus|
|2019||Semester One full semester||Block||Auckland Campus|
|2019||Semester One full semester||Block||Wellington Campus|
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