Postgraduate Course: Data Analytics with High Performance Computing (PGPH11089)
|School||School of Physics and Astronomy
||College||College of Science and Engineering
|Credit level (Normal year taken)||SCQF Level 11 (Postgraduate)
||Availability||Not available to visiting students
|Summary||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)
||Other requirements|| None
Course Delivery Information
|Academic year 2016/17, Not available to visiting students (SS1)
|Learning and Teaching activities (Further Info)
Lecture Hours 22,
Seminar/Tutorial Hours 11,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
|Additional Information (Learning and Teaching)
Please contact the School for further information
|Assessment (Further Info)
|Additional Information (Assessment)
||Hours & Minutes
|Main Exam Diet S2 (April/May)||2:00|
On completion of this course, the student will be able to:
- 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 and techniques applicable to data analytics.
- Be able to identify and apply appropriate data analytic techniques to a problem.
- Understand how data analytic techniques scale and perform on HPC infrastructures.
|Graduate Attributes and Skills
|Course organiser||Mr Terence Sloan
|Course secretary||Ms Joan Strachan
Tel: (0131 6)50 5030
© Copyright 2016 The University of Edinburgh - 3 February 2017 4:58 am