DATA 310 - Data Mining

Data mining is a multidisciplinary field focused on discovering patterns and associations in data. In this course students gain proficiency in supervised data mining techniques for building prediction models including decision trees, random decision forests, bootstrapping, training and testing using multi-fold cross validation and using entropy measures for weighting features. Students also learn to use unsupervised data mining techniques such as clustering and association analysis.

Prerequisites Complete DATA 200
Credits: 3