MSc Computational Data Science
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Key information
- Location
- Edinburgh
- Mode of delivery
- On-campus
- Delivery type
- Full-time, Part-time
- Start date
- September
- Duration
- 1 year
- Qualification
- MSc
Contact
Contact our enquiries team
Contact usOverview
MSc Computational Data Science harnesses Heriot-Watt's world-leading expertise in the statistical foundations of data science and associated computational techniques, with applications to imaging and vision, ecology and climate, and stochastic modelling.
You should study MSc Computational Data Science if you already have a degree in mathematics, engineering, physics or equivalent and want to develop skills at the cutting edge of data science.
This MSc programme will teach you the statistical foundations and computational techniques in data science. It will provide you with a unique set of skills to address data science challenges, for applications ranging from imaging and vision (pathway 1), to ecology and climate (pathway 2), or stochastic modelling (pathway 3).
Four reasons to study MSc Computational Data Science
- Data scientists, data engineers, and business analysts are among the most sought-after careers and Edinburgh is intended to be the 'Data Capital of Europe'.
- Students will acquire unique interdisciplinary knowledge at the interface between the statistical foundations of data science and associated computational techniques.
- Students will be exposed to specialised applications, ranging from imaging and vision, to ecology and climate, or stochastic modelling.
- This MSc is run by international leaders in their field.
A Partnership of World-Leading Departments
The MSc Computational Data Science is a joint degree between the Schools of Mathematical and Computer Sciences, and of Engineering and Physical Sciences. These schools are renowned for their world-class research in the statistical foundations and computational techniques in data science, with internationally leading expertise in imaging and vision, ecology and climate, stochastic modelling, and well beyond.
In the most recent Research Excellence Framework (REF2021), which assesses the quality of research of UK higher education institutions, Heriot-Watt University was ranked first in Scotland and third in the UK for both Engineering research and Mathematical Sciences research, through joint submissions with the University of Edinburgh.
The two universities host the Bayes Centre and the National Robotarium, two of Edinburgh's Data-Driven Innovation hubs.
More into High Performance Computing than Statistics?
If you are interested in the Imaging and Vision pathway, but are more interested in complementing your education with High Performance Computing rather than the foundational statistical aspects, check our joint MSc in Imaging, Vision and High Performance Computing between Heriot-Watt University and the University of Edinburgh.
Course content
Year 1
All students are required to take a total of three mandatory courses in Semesters 1 and 2, designed to equip students with the foundational tools of data science, with a first clear opening to applications, as well as with fundamentals of critical analysis and research preparation.
Students will be further required to select one elective course per semester, relating to applications in imaging and vision (pathway 1), ecology and climate (pathway 2), or stochastic modelling (pathway 3).
In semester 3, students will choose a project with primary supervisor in either the School of Mathematical and Computer Sciences, or the School of Engineering and Physical Sciences. Collaboration with industry is possible and encouraged.
Semester 1
- Optimisation and Deep Learning for Imaging and Vision I - 15 credits
- Statistical Machine Learning - 15 credits
- Statistical Models - 15 credits
One from:
- Foundations of Learning and Computer Vision (pathway 1) - 15 credits
- Mathematical Ecology (pathway 2) - 15 credits
- Probabilistic Methods (pathway 3) - 15 credits
Semester 2
- Optimisation and Deep Learning for Imaging and Vision II - 15 credits
- Critical Analysis and Research Preparation - 15 credits
- Bayesian Inference and Computational Methods - 15 credits
One from:
- Graph Methods for Imaging, Vision and Computing (pathway 1) - 15 credits
- Data Assimilation (pathway 2) - 15 credits
- Stochastic Networks (pathway 3) - 15 credits
Alternative courses: If you have studied any of the mandatory courses previously, you may be eligible to study alternative courses as agreed with the programme directors.
Semester 3
Dissertation in Computational Data Science - 60 credits
Go Global
Some of our Postgraduate Taught Masters Programmes are eligible for Inter-Campus Transfer. Please contact studywithus@hw.ac.uk for further information.
Fees and funding
Tuition fees
| Status [footnote 1] | Full-time | Part-time |
|---|---|---|
| UK | 10600 | 5300 |
| Overseas [footnote 2] | 24496 | 12248 |
Scholarships and bursaries
We aim to encourage well-qualified, ambitious students to study with us and we offer a wide variety of scholarships and bursaries to achieve this. Over £6 million worth of opportunities are available in fee and stipend scholarships, and more than 400 students benefit from this support.
View our full range of postgraduate scholarships.
Entry requirements
Entry
You will need a first or upper second-class honours degree (or its overseas equivalent) that has imparted reasonable know-how in programming and mathematics. Suitable candidates are likely to have studied a first degree in mathematics, statistics, physics, engineering or computer science. Lesser qualifications combined with relevant work experience may also be suitable.
English language requirements
Important: If your first language is not English, or your first degree was not taught in English, we'll need to see evidence of your English language ability.
The minimum requirement for English language is IELTS 6.5, we also accept TOEFL (scores of 79 and higher).
We also offer a range of English language courses to help you meet the English language requirement prior to starting your master's programme:
- 20 weeks English (for IELTS of 5.0 with no skill lower than 4.5)
- 14 weeks English (for IELTS of 5.0 with minimum of 5.0 in writing and no skill lower than 4.5)
- 10 weeks English (for IELTS of 5.5 with no skill lower than 5.0)
- 6 weeks English (for IELTS 5.5 with no skill lower than 5.5)
Why Heriot-Watt?
Clubs and activities
At Heriot-Watt we have an extensive programme of over 90 sports clubs and societies to get involved in. You can try as many as you like or start one of your own!
Edinburgh campus accommodation
Living on our beautiful Edinburgh campus is a great way to meet others from all over the world. Our student accommodation is within easy reach of the teaching buildings, sports facilities, catering venues and the Student Union
Edinburgh Campus facilities
At Heriot-Watt University, we offer a wide range of services to help you deal with all aspects of your life with us, whether these be academic, personal, technical, financial or just plain fun!
Health and wellbeing
Student Wellbeing Services aim to provide a range of support, guidance, activities and advice to help students to be their best, and get the most from their university experience. From counselling to coaching we'll sure you are fully supported.
Heriot-Watt Student Union
There's more to university life than simply getting your degree, and Heriot-Watt University Student Union is all about helping you to have the best possible experience while you study here.
Living in Edinburgh
Historic and beautiful, a cultural capital that fuels the senses whether you love the arts or nature, nightlife or study life, Edinburgh is stimulating and inspirational and never, ever boring.
Sport facilities
Oriam, Scotland Sports Performance Centre offers state-of-the-art sports facilities, for all levels of sport and ability
Your career
Employers around the world actively seek out our graduates because they are work-ready. All our degrees are career-focused and relevant to the needs of industry. Around 95% of our students are in employment or further study within six months of graduating.