ITS 836 - Data Science and Big Data Analytics

Credit(s): 3 hours
In this course the students explore key data analysis and management techniques, which applied to massive datasets are the cornerstone that enables real-time decision making in distributed environments, business intelligence in the Web, and scientific discovery at large scale. In particular, students examine the map-reduce parallel computing paradigm and associated technologies such as distributed file systems, no-sql databases, and stream computing engines. This highly interactive course is based on the problem-based learning philosophy. Students are expected to make use of technologies to design highly scalable systems that can process and analyze Big Data for a variety of scientific, social, and environmental challenges.