MSc Data Analysis for Life and Environmental Scientists

Duration Full time 1 year
Part time 2 years
  • College of Life and Environmental Sciences
LocationExeter (Streatham)
Start date September


Data Analysis for Life and Environmental Scientists allows postgraduates to blend their life and environmental sciences expertise with the computing, coding, and data skills needed to pursue analytical careers in the science, industry, environment, and business sectors.

The curriculum introduces life scientists to essential computing and data science concepts such as computer servers, coding, data management and high-level analysis, while also allowing specialization in particular life sciences areas such as Biology, Geography, Psychology, Sports, and Health.

Students on the programme will conduct independent research under the tutelage of world-class academics, produce a research paper suitable for publication in academic journals, and develop the transferable skills necessary for professional data analysts in academia and industry. These personal development and networking opportunities will ensure that graduates of the programme are highly competitive whether applying for jobs or postdoctoral positions.

Why study Data Analysis at the University of Exeter?

  • Our lecturers are leaders in their research areas
  • We have close links with a wide range of collaborators, including educational institutions, industries, NGOs, charities, alumni, and government
  • We have state-of the art facilities
  • You will receive hands-on careers advice while you are here and graduate with excellent employment prospects

I can’t overstate how much I have benefitted from learning coding and computational skills. These skills have been critical in allowing me to manipulate and analyse large datasets of different types. They have enabled me to participate in a wide variety of research, and given me a base which makes the learning of new analysis easier. Having practical coding skills has also led me to ask questions that would otherwise be completely impractical to address. I’d highly recommend anyone thinking of pursuing any type of research or career to develop these skills!

Dr Julian C. Evans, Biosciences Postgraduate.

Programme structure

Compulsory modules

150 credits of compulsory modules

Coding 15
Computing 15
Data Handling and Data Analysis 15
Think Tank 15
Literature Review 30
Research Project 60

Optional modules

30 credits of optional modules.

Students will choose two subject specialism modules from a list that is still being finalised.

The modules we outline here provide examples of what you can expect to learn on this degree course based on recent academic teaching. The precise modules available to you in future years may vary depending on staff availability and research interests, new topics of study, timetabling and student demand.

Learning and teaching

This programme consists of three blocks of education. The first block (Data Science) will introduce the coding, computing, and data skills you will need to become a data scientist. The second block (Scholarship) will allow you to explore subject specialisms in the research areas of Biosciences, Geography, Psychology, and Sport and Health Sciences so that you can better think about how concepts in big data can be applied to, and advance, these fields. The third block (Research) allows you to become an expert in your chosen field, through the completion of a literature review and a research project tailored to your interests.

Teaching and learning methods

All material is designed for Masters level and will involve seminars, group discussion, laboratory sessions, and independent research. Within modules, there is considerable scope for you to direct your learning towards fields of particular interest—especially through your choice of research project.


Taught modules will be assessed using a range of methods, including formal oral presentation, written reports, discussions, and practical projects. A significant proportion of your assessment in the programme is based on the research project and associated literature review and oral presentation.


This programme is specifically designed to maximise your employability. You will develop a range of transferrable skills in computing, analysis, and your scientific area of expertise.

You will write a literature review that can be submitted to an academic journal, and perform a research project generating results that can be shared at a professional conference; these achievements will put you in good stead to compete for jobs and further postgraduate research opportunities.

You can take advantage of the University’s many research links in order to network with experts and organizations offering employment opportunities within the UK and abroad.

Entry requirements 2017

Normally at least a 2:1 Honours degree or equivalent in a relevant science subject is required, although a 2:2 with relevant experience will be considered.

Fees and funding

Tuition fees per year 2017/18

  • UK/EU: £9,600 full-time; £4,800 part-time
  • International: £19,200 full-time

Fee information

Fees can normally be paid by two termly instalments and may be paid online. You will also be required to pay a tuition fee deposit to secure your offer of a place, unless you qualify for exemption. For further information about paying fees see our Student Fees pages.

UK government postgraduate loan scheme

Postgraduate loans of up to £10,000 are now available for Masters degrees. Find out more about eligibility and how to apply.

Contact us

We welcome enquiries about the course. For further information contact:

Phone: +44 (0)1392 725818

Future Data Analyst Scholarship

We have scholarships of £3,000 payable as a reduction on tuition fees for full-time applicants.
Apply now.