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161.777 Practical Data Mining (15 credits)

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.

Requirements Requirements help

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.

Offerings Offerings help

Year Semester Mode Location
2020 Semester One full semester Block Auckland Campus
2020 Semester One full semester Block Wellington Campus

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