Postgraduate Course: Data Analytics with High Performance Computing (PGPH11089)

Course Outline
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
SCQF Credits	10	ECTS Credits	5
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
Course description	Not entered

Entry Requirements (not applicable to Visiting Students)
Pre-requisites		Co-requisites
Prohibited Combinations		Other requirements	None

Course Delivery Information

Academic year 2017/18, Not available to visiting students (SS1)		Quota: None
Course Start	Semester 2
Timetable	Timetable
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 Information (Learning and Teaching)	Please contact the School for further information
Assessment (Further Info)	Written Exam 100 %, Coursework 0 %, Practical Exam 0 %
Additional Information (Assessment)	100% examination
Feedback	Not entered
Exam Information
Exam Diet	Paper Name		Hours & Minutes
Main Exam Diet S2 (April/May)			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. 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.