This course introduces students to data mining and its various applications. Students will gain proficiency in supervised data mining techniques for building prediction models such as decision trees, random decision forests, bootstrapping, training and testing using multi-fold cross-validation and using entropy measures for weighting features. Students will also use unsupervised data mining techniques such as clustering and association analysis. Both methods are used for discovering patterns and associations in data.
Prerequisite: Complete DAT-210