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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2018/2019

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DRPS : Course Catalogue : School of Physics and Astronomy : Postgraduate (School of Physics and Astronomy)

Postgraduate Course: Data Analytics with High Performance Computing (PGPH11089)

Course Outline
SchoolSchool of Physics and Astronomy CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryData Analytics, Data Science and Big Data are a just a few of the many topical terms in business and academic research, all effectively referring to the manipulation, processing and analysis of data. Fundamentally, these are all concerned with the extraction from data of knowledge that can be used for competitive advantage or to provide scientific insight. In recent years, this area has undergone a revolution in which HPC has been a key driver, as evidenced by the vast clusters that power Google and Amazon as well as the supercomputing tiers analysing the outputs from the Large Hadron Collider. This course provides an overview of data science and the analytical techniques that form its basis as well as exploring how HPC provides the power that has driven their adoption.

The course will cover:
- Key data analytical techniques such as, classification, optimisation, and unsupervised learning
- Key parallel patterns, such as Map Reduce, for implementing analytical techniques
- Relevant HPC and data infrastructures
- Case studies from academia and business
Course description Not entered
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Not being delivered
Learning Outcomes
On completion of this course, the student will be able to:
  1. Understand what data analytics, data science and big data are.
  2. Have knowledge of the common, popular, important data analytics techniques.
  3. Have knowledge of the common, popular, important HPC infrastructures and techniques applicable to data analytics.
  4. Be able to identify and apply appropriate data analytic techniques to a problem.
  5. Understand how data analytic techniques scale and perform on HPC infrastructures.
Reading List
None
Additional Information
Graduate Attributes and Skills Not entered
KeywordsDAwHPC
Contacts
Course organiserMr Terence Sloan
Tel:
Email: T.M.Sloan@ed.ac.uk
Course secretaryMr Ben Morse
Tel: (0131 6)51 3398
Email: Ben.Morse@ed.ac.uk
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