Postgraduate study

High Performance Computing with Data Science (Online Learning) MSc, PgDip (ICL), PgCert (ICL), PgProfDev

Awards: MSc, PgDip (ICL), PgCert (ICL), PgProfDev

Study modes: Part-time Intermittent Study

Online learning

This programme aims to provide students with in-demand (for both a wide range of industries and academic disciplines) skills and knowledge of the techniques and technologies underpinning parallelism and High Performance Computing (HPC).

HPC is the use of powerful processors, networks and parallel supercomputers to tackle problems that are very computationally or data-intensive. Data Science involves the manipulation, processing and analysis of data to extract knowledge, and High Performance Computing (HPC) provides the power that underpins it. You will learn leading-edge HPC technologies and skills to exploit the full potential of the world’s largest supercomputers and multicore processors. This is a well-established programme that has been successful in training generations of specialists in parallel programming. High Performance Computing is a key area supporting most areas of scientific research and industry.

The flexible structure ensures students acquire core principles required before proceeding to their choice of more advanced topics and allows students to take on the programme at their own pace. You can study to an MSc, Postgraduate Diploma, Postgraduate Certificate or Postgraduate Professional Development level (further information in the programme structure section, below).

EPCC is the UK’s leading supercomputing centre with staff who are experienced HPC practitioners and is a major provider of HPC training in Europe with an international reputation for excellence in HPC education and research and a well-established on-campus MSc programme that has been successful in training generations of specialists in parallel programming. Students benefit from access to HPC systems with recent examples including ARCHER (the UK national Tier 1 supercomputing service with over 100,000 cores) and Cirrus, an heterogeneous system EPSRC Tier-2 National HPC Facility.

For an insight into EPCC’s current, including cutting edge, systems please our website.

Our online learning technology is fully interactive, award-winning and enables you to communicate with our highly qualified teaching staff from the comfort of your own home or workplace.

Our online students not only have access to the University of Edinburgh’s excellent resources, but also become part of a supportive online community, bringing together students and tutors from around the world.

This programme will not require you to run code locally as you will have access to HPC systems provided as part of the programme, however, the ability to code on your device is required - therefore a laptop or desktop computer running Windows, iOS, or Linux is recommended.

Studying online at Edinburgh

Find out more about the benefits and practicalities of studying for an online degree:

This programme is available on a part-time intermittent basis: i.e. it is inherently flexible in nature.

During the taught component students are permitted to take up to 30 credits per University semester (Semester 1 runs from early/mid-September to mid-December, Semester 2 runs from early/mid-January to mid/late-May), but in an individual Semester may take zero credits of courses. As fees are due at course level this means that students have flexibility both financially and in terms of their time commitment.

The credit sizes and course availability listed below may be subject to change, but are not expected to. Should this occur applicants/students would be given as much notice as possible.

MSc Structure

The MSc programme may be completed in as little as three academic years or as many as six, with the taught component (comprising 120 credits of taught courses) taking between two and five years and the dissertation component (comprising a 60 credit dissertation course) taking one year.

MSc compulsory courses:

  • Practical Introduction to Data Science (20 credits, Semesters 1 & 2)
  • Practical Introduction to High Performance Computing (20 credits, Semesters 1 & 2)
  • Message Passing Programming (10 credits, Semester 2)*
  • Threaded Programming (10 credits, Semester 2)*
  • Programming Skills (10 credits, Semester 1)
  • Software Development (10 credits, Semester 1)
  • Project Preparation (10 credits, Semesters 1 & 2 – but most workload occurs in Semester 2)**
  • Dissertation (60 credits: September-August) ***

*Requires Practical Introduction to High Performance Computing as pre/co-requisite. **Not guaranteed to be available in Academic Year 2020/21. Expected to launch at latest in Academic Year 2021/22, subject to approval at Board of Studies. ***Not guaranteed to be available in Academic Year 2020/21 or Academic Year 2021/22. Expected to launch at latest in Academic Year 2022/23, subject to approval at Board of Studies.

MSc optional courses:

  • Parallel Design Patterns (10 credits, Semester 1) * ~
  • Performance Programming (10 credits, Semester 1) * ~
  • Design and Analysis of Parallel Algorithms (10 credits, Semester 1) *
  • Advanced Parallel Techniques (10 credits, Semester 1) * ~
  • Plus some optional courses available from School of Informatics offerings (subject to availability)

**Not guaranteed to be available in Academic Year 2020/21. Expected to launch in Academic Year 2021/22, subject to approval at Board of Studies. ~Requires a prerequisite course or course(s) from the Compulsory Courses.

PGDip Structure

The PGDip programme may be completed in as few as two academic years or as many as four. The PGDip comprises the MSc programme taught component (comprising 120 credits of taught courses) and has compulsory/optional course options the same as above, but with the only difference being that the Project Preparation Course is optional for PGDip students, but compulsory for MSc students and that PGDip students do not take a dissertation course.

PGCert Structure

The PGCert programme may be completed in as few as one academic year or as many as two. It comprises 60 credits, all compulsory:

Compulsory Courses:

  • Practical Introduction to Data Science (20 credits, Semesters 1 & 2)
  • Practical Introduction to High Performance Computing (20 credits, Semesters 1 & 2)
  • Message Passing Programming (10 credits, Semester 2)*
  • Threaded Programming (10 credits, Semester 2)*

*Requires Practical Introduction to High Performance Computing as pre/co-requisite.

PPD Structure

Postgraduate Professional Development (PPD) is an unstructured programme of study allowing students to take up to 50 credits of courses from the PGCert Degree Programme Table (DPT) (see list of available courses above) over up to two academic years. The PPD does not offer a final accredited exit award, but certificates for modules completed can be provided. Students interested in an accredited award may wish to instead apply for the PGCert, although a student enrolled on the PPD may apply to transfer to the PGCert.

The learning outcomes of the programme are to:

  • Equip students with an understanding of HPC architectures and technologies.
  • Equip students with expertise in advanced tools and techniques for HPC and Data Science software development.
  • Enable students to apply this knowledge in order to exploit modern parallel and multicore computing systems and Data Science techniques in key scientific and commercial application areas.
  • Enable students to develop skills in problem-solving, project management, independent and critical thinking, team work, professionalism and communication.
  • Enable students to develop as HPC and Data Science practitioners, able to apply current and emergent technologies in both industry and research.
  • Teach the leading-edge programming techniques required to exploit the power of the world’s largest parallel supercomputers.

Graduates from EPCC’s on-campus MSc programmes are in high demand from a wide range of companies ranging from multinationals to SMEs both within the UK, Europe, and internationally as well as a strong demand from within academia both as researchers within HPC, computational science fields, data science, and professionally for HPC services and centres underpinning research.

Initial graduate destinations for on-campus students over recent years include: ARM, Intel, Amazon, MathWorks, NCR, Avaloq, Global Surface Intelligence, Boston Ltd, ECMWF, Leonardo, STFC, ICHEC, and, EPCC itself with 10 current EPCC staff being graduates of the on-campus programme. Many students also go on to further study opportunities, with 8 current University of Edinburgh PhD students being graduates of the programme.

A UK 2:1 honours degree, or its international equivalent, in a relevant subject such as computer science and informatics, physics, mathematics, engineering, biology, chemistry and geosciences.

You must be a competent programmer in at least one of C, C++, Python, Fortran, or Java and should be familiar with mathematical concepts such as algebra, linear algebra and probability and statistics.

We will also consider your application if you don’t have formal programming training (e.g. if you are primarily self-taught), or if you have a 2:2 honours degree with high marks in computational courses and/or additional relevant work experience. Your application should clearly demonstrate your relevant experience.

International qualifications

Check whether your international qualifications meet our general entry requirements:

English language requirements

You must demonstrate a level of English language competency at a level that will enable you to succeed in your studies, regardless of your nationality or country of residence.

English language tests

We accept the following English language qualifications at the grades specified:

  • IELTS Academic: total 6.5 with at least 6.0 in each component

  • TOEFL-iBT: total 92 with at least 20 in each section

  • PTE Academic: total 61 with at least 56 in each of the Communicative Skills scores

  • CAE and CPE: total 176 with at least 169 in each paper

  • Trinity ISE: ISE II with distinctions in all four components

Your English language qualification must be no more than three and a half years old from the start date of the programme you are applying to study, unless you are using IELTS, TOEFL, PTE Academic or Trinity ISE, in which case it must be no more than two years old.

Degrees taught and assessed in English

We also accept an undergraduate or postgraduate degree that has been taught and assessed in English in a majority English speaking country, as defined by UK Visas and Immigration:

We also accept a degree that has been taught and assessed in English from a university on our list of approved universities in non-majority English speaking countries.

If you are not a national of a majority English speaking country, then your degree must be no more than three and a half years old at the beginning of your programme of study.

Find out more about our language requirements:

AwardTitleDurationStudy mode
MScHigh Performance Computing with Data ScienceUp to 6 YearsPart-time Intermittent StudyTuition fees
PgDip (ICL)High Performance Computing with Data ScienceUp to 4 YearsPart-time Intermittent StudyTuition fees
PgCert (ICL)High Performance Computing with Data ScienceUp to 2 YearsPart-time Intermittent StudyTuition fees
PgProfDevHigh Performance Computing with Data ScienceUp to 2 YearsPart-time Intermittent StudyTuition fees

Search for scholarships and funding opportunities:

  • EPCC
  • The Bayes Centre
  • 47 Potterrow
  • Central Campus
  • Edinburgh
  • EH8 9BT

We encourage you to apply at least one month prior to entry so that we have enough time to process your application. If you are also applying for funding or will require a visa then we strongly recommend you apply as early as possible. We may consider late applications if we have places available, but you should contact the relevant Admissions Office for advice first.

You must submit one reference with your application.

Find out more about the general application process for postgraduate programmes:

Further information

  • EPCC
  • The Bayes Centre
  • 47 Potterrow
  • Central Campus
  • Edinburgh
  • EH8 9BT