Data Mining & Predictive Analytics

Catalog description

Prerequisite: IST 331. Introduces data mining methods, tools and techniques. Topics include acquiring, parsing, filtering, mining, representing, refining, and interacting with data. It covers data mining theory and algorithms including linear regression, logistic regression, rule induction algorithm, decision trees, kNN, Naive Bayse, clustering. In addition to discriminative models such as Neural Network and Support-Vector Machine (SVM), Linear Discriminant Analysis (LDA) and Boosting, the course will also introduce generative models such as Bayesian Network. It also covers the choice of mining algorithms and model selection for applications. Hands-on experience include the design and implementation, and explorations of various data mining and predictive tools.