Data Science II An introduction to advanced analytics techniques in data science, including random forests, semi-supervised learning, spectral analytics, randomized algorithms, and just-in-time compilers. Distributed and out-of-core processing. Offered every year. Credit Hours: 4 Prerequisites: CSCI 3360 Level: Graduate Undergraduate Course Information File: CIS_CSCI_4360_0.pdf (140.44 KB)
