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:

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Credit Hours:
4
Course Information File: