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STAT 208: Statistical Data Mining Methods

This course combines multiple disciplines containing machine learning, data mining and statistical techniques to provide the basic foundation for structuring, understanding, and using large datasets effectively and efficiently.  It covers principle data-mining methodologies, major software tools, and applications of statistics to data mining.

The course includes: Bayes and LDA classifiers, logistic regression and neural network classifiers, support vector classifiers, classification trees, predictive modeling, ridge and lasso regressions, k-means and Dendrogram clustering methods, business analytics and mining association rules. By the end of this course, students will have the necessary knowledge to use advanced statistical techniques to analyze large data sets in different disciplines and produce scientifically viable results.