15 Credits SPRING

Aims/Description: Data Science techniques often need to be applied to large amounts of data to generate insights. To deal with volume, velocity, and variety of data we need to rely on novel computational architectures that focus on scaling-out data processing as compared to the classic scale-up approach. Such systems allow to add computational resources to a distributed system depending on requirements and load which changes over time. In this module we will give students knowledge about modern scale-out system architectures to perform data analytics queries over very large structured/unstructured datasets as well as to run data mining algorithms at scale.

Staff Contact: Dr. Frank Hopfgartner
Teaching Methods: Lectures, Problem solving, Laboratory work, Independent Study
Assessment: Course work
WebCT resources are available for this module

Information on the department responsible for this unit (Information School):

Departmental Home Page
Teaching timetable


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