Cyber Infrastructure for Big Data Analytics
Big data is typically defined by three main characteristics: volume, velocity and variety. As such, there is significant complexity within the data and the underlying systems that host and analyze this information. These systems demand accurate, timely, cost-effective, innovative forms of information processing for enhanced insight and decision-making at enterprise scales of operation. This is also a rapidly developing and continually evolving process for analytics within organizations where simple data warehousing and data mining techniques of the past are no longer sufficient. Due to these factors, this course will address the architectures, algorithms, and implementations of popular Big Data systems, such as Hadoop/MapReduce, and will include analysis of real-world data sets, such as Twitter, Netflix, the Human Genome Project, U.S. Census Data, Google PublicData, etc.
Prerequisites: None listed
Typically offered: Ask instructor
Contact Prof. Tom Hacker or Prof. Ray Hansen for additional information about this course.
Credit Hours:
3
Department:
Computer and Information Technology
Location:
West Lafayette