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.
Prerequisites:
Credit Hours:
4
Course Information File: