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DRPS : Course Catalogue : School of Informatics : EPCC on-campus

Postgraduate Course: High Performance Data Analytics (EPCC11014)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityAvailable to all students
SCQF Credits10 ECTS Credits5
SummaryData 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
High Demand Course? Yes
Course Delivery Information
Academic year 2024/25, Available to all students (SV1) Quota:  84
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 Analytics2:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. Understand and plan the stages of a data science/analytics projects
  2. Know common, popular, and important data analytics/machine learning techniques.
  3. Identify, apply and evaluate appropriate data analytic techniques to a problem.
  4. Know how HPC software and infrastructure can help make data analytics/ML techniques scalable
  5. 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.
KeywordsData Analytics,HPC,High Performance Computing,EPCC,HPCwDS,DAwHPC,Big Data,Parallelism,HPDA
Course organiserDr Darren White
Tel: (01316)51 3415
Course secretaryMr James Richards
Tel: 90131 6)51 3578
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