CS433: Big Data Analytics
Credits: 3
Catalog Description
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.
Prerequisites
- CS310, MATH 260, and MATH345; or permission of the instructor.
Upper-level course in big data analytics for advanced undergraduates covering large-scale data analysis and modern machine learning methods.