Course: Data Mining - Unsupervised Techniques


  • 11 October 2013
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Aim of Course:

Data mining, the art and science of learning from data, covers a number of different procedures. This course covers key unsupervised learning techniques: association rules, principal components analysis, and clustering. (Introduction to Predictive Modeling covers techniques that are used to predict a record's class, or the value of an outcome variable on the basis of a set of records with known outcomes). The course will include an integration of supervised and unsupervised learning techniques.

This is a hands-on course -- participants in the course will have access to an Excel-based comprehensive tool for data-mining, XLMiner, the use of which will be explained in the course. Participants will apply data mining algorithms to real data, and will interpret the results.

An online bulletin board available enables you to interact with the instructor and your fellow students throughout the course and submit your own findings for discussion. The course should take about 15 hours per week. Regular visits to the course discussion board are required, but you can arrange these at your own convenience. (Follow-up consultation is available after completion of the course for an additional fee.)

Please note this is an online course.

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