CISC560

In a time where data is produced in massive amounts by large sensor networks, new data acquisition techniques, simulations, and social networks, to name a few, efficiently extracting, interpreting, and learning from very large datasets requires a new generation of scalable algorithms as well as new data management technologies. Students explore the concept of Big Data and the current trends, applications, and challenges associated with it. In addition, students explore key data analysis and management techniques, which applied to Big Data are the cornerstone that enables real-time decision making in distributed environments, business intelligence in the Web, and large scale scientific discovery. Students explore the map-reduce parallel computing paradigm and associated technologies such as distributed file systems, no-sql databases, and stream computing engines. Students design highly scalable systems that can process and analyze Big Data for a variety of scientific, social, and environmental challenges.

In-Person On-Campus

Standard (S)

School of Liberal Arts