Courses

Data Mining: Unsupervised Techniques, at statistics.com

By Dr. Anthony Babinec

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 Data Mining: Supervised Learning covers techniques that are used to predict a records class, or the value of an outcome variable of interest 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 10 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.)

Who Should Take This Course:
Marketers seeking to specify customer segments and identify associations among products purchased, environment scientists seeking to cluster observations, analysts who need to identify the key variables out of many, MBA's seeking to update their knowledge of quantitative techniques, managers and scientists who want to see what data-mining can do, and anyone who wants a practical hands-on grounding in basic data-mining techniques.

For more information and to register, see

http://www.statistics.com/courses/datamining2/