Techniques for data science. Topics include unsupervised techniques and basic predictive modeling techniques. Prerequisites: DAS 251, 252, 253, 350.

Predictive modeling techniques for data science. Topics include tree models, shrinkage techniques, support vector machine, neural network, deep learning and Naïve Bayes. Prerequisites: DAS 450.

Students work on independent research projects. Problems may be from internship experience, prior courses or another source, subject to instructor approval. Prerequisites: DAS 460; Senior Standing.