3.00 Credits
This course covers advanced study and practice in data mining and predictive analytics. Topics include understanding, configuring, and applying advanced variants of data association, classification, clustering, and statistical analysis engines, analyzing and applying underlying machine learning algorithms, exploring instance-based, support vector, time-series, ensemble, graphical, and lazy learning algorithms, meta-learning, neural nets, genetic algorithms, and validating results. The course examines topics specific to very large data sets. Data cleaning and formatting require some programming in a modern scripting language. Other course activities include using, extending, and customizing off-the-shelf machine learning software systems to accomplish the tasks of data analysis.