| UCAS code | 1234 |
|---|---|
| Duration | 1 year full time |
| Entry year | 2024 |
| Campus | Penryn Campus |
| Discipline | Mathematics |
| Contact |
| Typical offer | A good degree (normally a 2:2). |
|---|---|
Overview
- Develop expertise in data-driven perspectives on ecology, evolution, conservation, biodiversity, and epidemiology
- Based at our stunning Penryn Campus in Cornwall and run jointly by the University’s Environment and Sustainability Institute and the Centre for Ecology and Conservation
- Be exposed to a wide variety of data and data analytics approaches with opportunities to work with industry, charities, or the public sector
- You’ll be immersed in the “Big Data revolution” and develop expertise in data-driven perspectives on ecology, evolution, conservation, biodiversity, and epidemiology
- Gain the skills and experience you need to succeed in the demanding and fast growing data analytics sectors particularly in relation to ecology and evolution
9th in the world for Ecology
ShanghaiRankings Global Ranking of Academic Subjects 2022
Top 20 in the UK for Mathematics
16th in the Complete University Guide 2024
Top 20 in the UK for world-leading research in Biological Sciences
REF 2021, based on 4-star research
Wide range of exciting and high-impact research projects
Entry requirements
A good degree (normally a 2:2).
Successful applicants will usually have at least an A-level or equivalent in Mathematics and/or have received quantitative skills training as part of their undergraduate programme, or possess relevant professional experience.
Prior experience of coding is not necessary on this course.
Entry requirements for international students
English language requirements
International students need to show they have the required level of English language to study this course. The required test scores for this course fall under Profile B3. Please visit our English language requirements page to view the required test scores and equivalencies from your country.
Markus is the Programme Director and his research addresses mathematical systems and control theory and their applications within marine engineering, renewable energy, systems biology and human wellbeing.
His academic work includes developing the Applied Mathematics programmes for both undergraduate and postgraduate taught students at the Penryn Campus, with a focus on interdisciplinary applications and in close liaison with colleagues.
Read more from Dr Markus Mueller
Dr Markus Mueller
Programme Director
Course content
Contemporary crises of the environment, and the ongoing threats of habitat loss and extinction posed to many species, have brought into sharp focus the importance of data intensive research in ecology and evolution. This programme is specifically designed for students with an interest in modern approaches in the life sciences.
- Term 1: students develop core skills and understanding through the fundamentals modules in data science and in ecology and evolution
- Term 2: students are exposed to state of the art methods in the Trends in Data Science and Artificial Intelligence module, complete the interdisciplinary, inquiry-led module module Tackling Sustainability Challenges using Data and Models, and can select from further Masters level modules within biosciences
- Term 3: students undertake an advanced data science and modelling project
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.
Compulsory modules
| Code | Module | Credits |
|---|---|---|
| MTHM601 | Fundamentals of Data Science | 30 |
| MTHM602 | Trends in Data Science and AI | 15 |
| MTHM603 | Data Science and Modelling Dissertation | 60 |
| MTHM604 | Tackling Sustainability Challenges using Data and Models | 30 |
| BIOM4046 | Evolutionary and Behavioural Ecology: Frontiers and Approaches | 30 |
Optional modules
| Code | Module | Credits |
|---|---|---|
| Select 15 credits: | ||
| LESM005 | Applied Data Analysis | 15 |
| BIOM4014 | Preparing for Ecological Consultancy | 15 |
| GEOM406 | Marine and Coastal Sustainability | 15 |
| BIO3131 | Trends in Ecology and Evolution | 15 |
| BIO3401 | Coevolutionary Interactions | 15 |
| BIO3415 | Ecological Responses to Climate Change | 15 |
| BIO3421 | Animal Migration | 15 |
Fees
2024/25 entry
UK fees per year:
£12,100 full-time
International fees per year:
£27,500 full-time
Office for Students Data Science Scholarship 2024
Eligible students from under-represented groups in the data science and AI sectors can apply for one of 16 £10,000 grants funded by the Office for Students (OfS) Data Science Scholarship. The scholarships are part of the OfS nationwide drive to expand access to this dynamic sector for women, students of colour, those with disabilities and those from lower socioeconomic backgrounds.
Find out more about this scholarship and apply here.
Scholarships
We invest heavily in scholarships for talented prospective Masters students. This includes over £5 million in scholarships for international students, such as our Global Excellence Scholarships*.
For more information on scholarships, please visit our scholarships and bursaries page.
*Selected programmes only. Please see the Terms and Conditions for each scheme for further details.
Teaching and research
We believe every student benefits from being taught by experts active in research and practice. You will discuss the very latest ideas, research discoveries and new technologies in seminars and via using state-of-the-art facilities and software and you will become actively involved in a research project yourself. The programme is led by the Centre for Environmental Mathematics and taught in collaboration with the Centre for Ecology and Conservation (CEC). You can find out more about our internationally recognised scientific research and industrial, public and charitable sector collaborations at the dedicated web pages for Environmental Mathematics and the CEC.
Careers
This programme is aimed primarily at Life Science graduates looking to further develop expertise in ecology, evolution, conservation, biodiversity, or epidemiology, and to tap into or up-skill for the “Big Data revolution” in the field and in general.
The UK has one of the world’s strongest and most developed data analytics sectors, predicted to grow by 177% over the next 5 years and demand for qualified professionals continues to grow. The UK’s commitment to expansion of renewable energy is likely to mean a high level of investment in the sector in the next decade. The adoption of the UK’s microgeneration tariff in 2009, the Green Deal in 2013, the phased adoption of the Renewable Heat Incentive from 2011-2014 and introduction of Contracts for Difference in 2014, suggests continued strong support for rapid expansion of renewable energy in the UK.
National and international job opportunities
Internationally, many other countries are making similar investments with major industrial nations including the US, India and China investing heavily in renewable generation. This investment will create broad opportunities for those seeking to work in the sector, both nationally and internationally.
Employer-valued skills
- Data science, modelling and essential programming skills and how to apply these to The United Nations sustainable development goals
- Ability to extract information from data as a basis for evidence-based decision making
- Working with stakeholders and advanced team-working skills in complex organisations
- Using digital media to convert complex data sets into useful information to engage with the lay public and specialists
Students will receive support from our dedicated Career Zone team, who provide excellent career guidance at all stages of career planning. The Career Zone provides one-on-one support and is home to a wealth of business and industry contacts. Additionally, they host useful training events, workshops and lectures which are designed to further support you in following your chosen career path.
Professor Townley is particularly interested in dynamical systems and control: the study of things (mechanical, biological, electrical, etc.) which interact and evolve in time and can be predicted, managed and optimised.
He’s been the Chair in Applied Mathematics at the Environment and Sustainability Institute (ESI) since June 2011. He works closely with colleagues in the College of Engineering, Mathematics and Physical Sciences, as well as in the College of Life and Environmental Sciences, to understand natural and man-made systems.
Read more from Professor Stuart Townley
Professor Stuart Townley
Professor in Applied Mathematics
Related courses of interest
The University of Exeter has significant teaching and research interests in the field of Data Science, and is home to the Institute of Data Science and Artificial Intelligence. The Institute of Data Science and Artificial Intelligence (IDSAI) provides a hub for data-intensive science and artificial intelligence (AI) activity within the University and the wider region.
Their vision is to develop innovative approaches to the use of data and artificial intelligence in modern society, covering the entire spectrum from collection through interrogation and analysis, to interpretation, visualisation and communication. They undertake fundamental research in data science and AI and facilitate interactions between data science researchers and problem owners to develop innovative approaches to the use of data in modern society, considering the social implications of data science and AI as well as the technical aspects.
To achieve this they work closely with other researchers, industry and government, for instance with key partners such as the Alan Turing Institute. Students have access to excellent facilities spanning a wide range of machine types and software ecosystems, as well as have the opportunity to study courses at either our Streatham Campus in Exeter or our Penryn Campus in Cornwall.
For more information see our related courses of interest page.
