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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2018/2019

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DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Data Analytics with High Performance Computing (INFR11171)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryData Analytics, Data Science and Big Data are 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.
Course description The course will cover:
- Key data analytical techniques such as, classification, optimisation, and unsupervised learning
- Key parallel patterns, for implementing analytical techniques
- Relevant HPC and data infrastructures
- Case studies from academia and business
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisitesNone
High Demand Course? Yes
Course Delivery Information
Academic year 2018/19, Available to all students (SV1) Quota:  80
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Lecture Hours 20, Supervised Practical/Workshop/Studio Hours 10, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 68 )
Assessment (Further Info) Written Exam 100 %, Coursework 0 %, Practical Exam 0 %
Additional Information (Assessment) 100% Exam
Feedback Via practical class exercises and on final exam after completion of course.
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)Data Analytics with High Performance Computing2:00
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. Understand common, popular, and important data analytics techniques.
  3. Understand 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
Provided via Learn
Additional Information
Graduate Attributes and Skills Reflection on learning and practice.
Adaptation to circumstances.
Solution Exploration, Evaluation and Prioritisation.
Special Arrangements There are limited spaces on this course. Students not on the MSc in High Performance Computing or MSc High Performance Computing with Data Science should contact the course secretary to confirm availability and confirm that they have the required prerequisites before being enrolled on the course.

The course is available to PhD students for class-only study. PhD students requiring a form of assessment (e.g. SUPA/School of Physics and Astronomy CDT students) must contact the course secretary to confirm method of enrolment.
Additional Class Delivery Information 2x Lectures, 1x Practical per week (Weeks 1-10).
KeywordsData Analytics,HPC,High Performance Computing,EPCC,HPCwDS,DAwHPC,Big Data,Parallelism
Contacts
Course organiserMiss Ioanna Lampaki
Tel: (0131 6) 51 34 36
Email: i.lampaki@epcc.ed.ac.uk
Course secretaryMr Ben Morse
Tel: (0131 6)51 3398
Email: Ben.Morse@ed.ac.uk
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