Big Data Analytics (3 credits)


This course introduces methods and platforms for analyzing large amounts of data. Classical paradigms of parallel computing – such as multithreading, message passing, and accelerator programming – are presented. Machine learning and data mining techniques – such as regression, clustering, classification, and deep learning – are discussed. Platforms of computing with big data – such as graph databases, distributed file systems, and map-reduce – are introduced. This course prepares students to perform predictive modeling and explore large, complex datasets.

Pre-requisites:
CS310, MATH 260, and MATH345; or permission of the instructor.