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. This module 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: PREISS JUDITA
Teaching Methods: Laboratory work, Independent Study
Assessment: Course work, Classroom testing

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

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Teaching timetable

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NOTE
The content of our courses is reviewed annually to make sure it's up-to-date and relevant. Individual modules are occasionally updated or withdrawn. This is in response to discoveries through our world-leading research; funding changes; professional accreditation requirements; student or employer feedback; outcomes of reviews; and variations in staff or student numbers. In the event of any change we'll consult and inform students in good time and take reasonable steps to minimise disruption.

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Teaching methods and assessment displayed on this page are indicative for 2024-25.

Western Bank, Sheffield, S10 2TN, UK