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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2024/2025

Timetable information in the Course Catalogue may be subject to change.

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DRPS : Course Catalogue : School of Informatics : EPCC on-campus

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

This course will be closed from 31 July 2024

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
SummaryN.B. This course has been replaced by course EPCC11014 High Performance Data Analytics

Data 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Course description N.B. This course has been replaced by course EPCC11014 High Performance Data Analytics

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
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. 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
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.
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 James Richards
Tel: 90131 6)51 3578
Email: J.Richards@epcc.ed.ac.uk
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