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A practical approach to data mining with real life applications and case studies; analysis of moderate to large volumes of data; data warehousing and cleansing; descriptive and predictive modelling; classification and regression trees; neural networks; memory-based reasoning; dimension reduction; cluster analysis including self-organising maps; ensemble models with hybrid, bagging and boosting; basics of text mining; rare event prediction and time oriented analysis; extensive use of modern data mining software tools.
Note(s): Access to a Windows PC is required for analysis of data.
|2017||Semester one full semester||Block||Auckland Campus|
|2018||Semester one full semester||Block||Auckland Campus|
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