Postgraduate Course: High Performance Data Analytics (EPCC11014)
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 of knowledge, from data, 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. 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 regression, classification and clustering
- Hands on experience with training and evaluation of analytical techniques through practicals.
- Relevant HPC tools/software/ data infrastructures
- Hands on experience on the use of HPC software/tools for training and evaluation of analytical techniques through practicals using Python.
|
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 2024/25, Available to all students (SV1)
|
Quota: 65 |
| Course Start |
Semester 2 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Supervised Practical/Workshop/Studio Hours 11,
Feedback/Feedforward Hours 1,
Summative Assessment Hours 2,
Revision Session Hours 1,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
61 )
|
| 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 formative and summative assessment |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | High Performance Data Analytics | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand and plan the stages of a data science/analytics projects
- Know common, popular, and important data analytics/machine learning techniques.
- Identify, apply and evaluate appropriate data analytic techniques to a problem.
- Know how HPC software and infrastructure can help make data analytics/ML techniques scalable
- Understand and use common, popular, HPC tools and techniques applicable to data analytics.
|
Reading List
| Provided via Learn/Leganto |
Additional Information
| Graduate Attributes and Skills |
Reflection on learning and practice.
Adaptation to circumstances.
Solution Exploration, Evaluation and Prioritisation. |
| Keywords | Data Analytics,HPC,High Performance Computing,EPCC,HPCwDS,DAwHPC,Big Data,Parallelism,HPDA |
Contacts
| Course organiser | Miss Ioanna Lampaki
Tel: (0131 6) 51 34 36
Email: i.lampaki@epcc.ed.ac.uk |
Course secretary | Mr James Richards
Tel: 90131 6)51 3578
Email: J.Richards@epcc.ed.ac.uk |
|
|
University Homepage