Postgraduate Course: Data Analytics with High Performance Computing (INFR11171)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Available to all students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | 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 |
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-requisites | None |
| High Demand Course? |
Yes |
Course Delivery Information
|
| Academic year 2022/23, Available to all students (SV1)
|
Quota: 50 |
| 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
75 %,
Coursework
25 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
75% Written Exam
25% Coursework (1 assignment) |
| 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 Computing | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand what data analytics, data science and big data are.
- Understand common, popular, and important data analytics techniques.
- Understand common, popular, important HPC infrastructures and techniques applicable to data analytics.
- 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). |
| Keywords | Data Analytics,HPC,High Performance Computing,EPCC,HPCwDS,DAwHPC,Big Data,Parallelism |
Contacts
| Course organiser | Miss Ioanna Lampaki
Tel: (0131 6) 51 34 36
Email: |
Course secretary | Miss Jemma Auns
Tel: (0131 6)51 3545
Email: |
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