Postgraduate Course: Data Analytics with High Performance Computing (PGPH11089)
Course Outline
| School | School of Physics and Astronomy |
College | College of Science and Engineering |
| Course type | Standard |
Availability | Not available to visiting students |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Credits | 10 |
| Home subject area | Postgraduate (School of Physics and Astronomy) |
Other subject area | None |
| Course website |
None |
Taught in Gaelic? | No |
| Course description | Data 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 |
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
| Additional Costs | None |
Course Delivery Information
|
| Delivery period: 2014/15 Semester 2, Not available to visiting students (SS1)
|
Learn enabled: Yes |
Quota: None |
|
Web Timetable |
Web Timetable |
| Course Start Date |
12/01/2015 |
| Breakdown of Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 22,
Seminar/Tutorial Hours 11,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
65 )
|
| Additional Notes |
Please contact the School for further information
|
| Breakdown of Assessment Methods (Further Info) |
Written Exam
100 %,
Coursework
0 %,
Practical Exam
0 %
|
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S2 (April/May) | | 2:00 | |
Summary of Intended Learning Outcomes
On completion of this course you should:
- Understand what data analytics, data science and big data are.
- Have knowledge of the common, popular, important data analytics techniques.
- Have knowledge of the common, popular, important HPC infrastructures applicable to data analytics.
- Be able to identify and apply appropriate data analytic techniques to a problem.
- Be able to critically evaluate the analytical performance of a data analytic technique.
- Be able to identify and apply the most appropriate HPC infrastructure for a particular data analytic technique.
- Understand how data analytic techniques scale and perform on HPC infrastructures. |
Assessment Information
| 100% examination |
Special Arrangements
| None |
Additional Information
| Academic description |
Not entered |
| Syllabus |
Not entered |
| Transferable skills |
Not entered |
| Reading list |
Not entered |
| Study Abroad |
Not entered |
| Study Pattern |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Mr Terence Sloan
Tel:
Email: T.M.Sloan@ed.ac.uk |
Course secretary | Yuhua Lei
Tel: (0131 6) 517067
Email: yuhua.lei@ed.ac.uk |
|
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