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Ecology and Data Science MSc

London, Stratford (UCL East)

Prepare to address critical global environmental challenges with a sought-after skillset spanning data science and ecology, on this one-year MSc, drawing on expertise from across UCL, the Institute of Zoology and the Natural History Museum. 

UK students International students
Study mode
UK tuition fees (2025/26)
£24,100
£12,050
Overseas tuition fees (2025/26)
£39,800
£19,900
Duration
1 calendar year
2 calendar years
Programme starts
September 2025
Applications accepted
Applicants who require a visa: 14 Oct 2024 – 27 Jun 2025
Applications close at 5pm UK time

Applications open

Applicants who do not require a visa: 14 Oct 2024 – 29 Aug 2025
Applications close at 5pm UK time

Applications open

Apply for this course

Entry requirements

Normally, an upper second-class (2:1) UK Bachelor's degree in life sciences, environmental sciences or related subject area, or an overseas qualification of an equivalent standard. Applicants with an appropriate professional qualification and relevant work experience.

The English language level for this programme is: Level 2

UCL Pre-Master's and Pre-sessional English courses are for international students who are aiming to study for a postgraduate degree at UCL. The courses will develop your academic English and academic skills required to succeed at postgraduate level.

Further information can be found on our English language requirements page.

Equivalent qualifications

Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website.

International applicants can find out the equivalent qualification for their country by selecting from the list below. Please note that the equivalency will correspond to the broad UK degree classification stated on this page (e.g. upper second-class). Where a specific overall percentage is required in the UK qualification, the international equivalency will be higher than that stated below. Please contact Graduate Admissions should you require further advice.

About this degree

You’ll learn data science methods from the ground up, while gaining a broad understanding of ecological theory. Layer this with skills in sampling design and biodiversity monitoring methods, sensor design, statistical programming languages like R and Python, and the most up-to-date machine learning and AI tools, including deep learning and computer vision.

Rooted in UCL’s Department of Genetics, Evolution and Environment and housed in the People and Nature Lab at UCL East, you’ll also learn from academics across Robotics, Connected Environments and Geography – with added input and project opportunities from the Zoological Society of London and the Natural History Museum, plus other industry partners.

Apply your skills on field studies, in-depth problem-based group work and an extensive data-science dissertation project – so you’re prepared to lead the next generation of AI-equipped environmental scientists working to solve the critical ecological and environmental global challenges of our time.

Who this course is for

This interdisciplinary master’s is for ecologists who want to learn technological and computational skills, and for data scientists who want to develop their skills and apply them to critical environmental issues. It’s relevant to conservation-focused or data science careers across government, NGOs, charities, the private sector and academia.

Read about what it takes to study for a Master's at UCL

What this course will give you

The Ecology and Data Science MSc has been developed to address the pressing need for ecologists who can harness ever-evolving computational power and AI tools to monitor, manage and conserve our precious ecosystems and wildlife populations.  

Join this highly popular one-year programme to build a full-stack toolkit to implement the whole lifecycle of ecological analysis.  

What you can gain from this course: 

  • Study in UCL’s People and Nature Lab at the Queen Elizabeth Olympic Park, part of the Centre for Biodiversity and Environment Research, which is nested within UCL Genetics, Evolution and Environment. 
  • UCL is ranked 6th in the world for Biosciences (QS Rankings by Subject 2024).  
  • Develop the expertise needed to implement the latest tools and technologies in sampling design, monitoring, sensor design, statistical programming, machine learning and deep learning – to address ecological and environmental challenges across society.  
  • Gain real-world experience through collaborations with the Natural History Museum and the Zoological Society of London, and project opportunities with industry partners like Chirrup.ai, UK Centre For Ecology & Hydrology (UKCEH), Biodiversify Ltd, East London Waterworks, Google and Microsoft.  
  • Plan and conduct extensive independent research, and develop the skills to report your findings to a variety of audiences.  
  • Build the programme around the aspects of ecology and data science that most interest you, with optional specialisms in areas like behavioural ecology, science communication and nature-friendly urban design.
  • Gain hands-on experience of design and implementation through a week-long field project in Queen Elizabeth Olympic Park.
  • Leave well equipped to apply your sought-after mix of skills and expertise to conservation-based or data science careers in government, environmental charities and consultancies, Non-Governmental Organisations (NGOs), or to pursue a PhD.  
Read about what you'll get out of a graduate programme

The foundation of your career

This master’s will give you a broad knowledge base and specialist skillset across both ecological science and data science – a rare and highly valued combination that will stand you in excellent stead for a meaningful career. 

You’ll be equipped to work in any organisation that uses – or wants to use – data science to tackle environmental or social challenges, including environmental, restoration, or conservation groups, NGOs, tech companies and start-ups, local or central government agencies, museums and engineering firms. Some students go on to specialise further by pursuing a PhD in either data science or ecology. 

Employability

There is a rapidly growing demand for scientists with expertise in cutting-edge technological, statistical and computational tools to solve the ecological challenges of today.  

With a multidisciplinary skillset, project management skills, in-depth knowledge and practical experience of fieldwork and independent research, you'll be ready to join the next generation of data-savvy biologists driving progress in this time-critical field. 

Networking

You’ll have regular opportunities to connect, collaborate and build professional contacts as part of your master’s.  

  • Network with students and academics from within and beyond the faculty at divisional, departmental and other research seminars.
  • Take part in seminar series at the National History Museum and Zoological Society of London, and join regular social events organised by, and within, the three partner institutions.
  • Work and network with industry professionals involved in the Nature-Smart Challenge module, from environmental consultancies, wildlife NGOs, local community groups and local government.
  • Take part in careers events through UCL Careers during the academic year, and enhance your CV writing and interview skills.  
  • Meet alumni to hear about their experiences and how the course has helped them progress in their careers. 

Teaching and learning

You will learn through a broad suite of teaching approaches, including lectures, seminars incorporating problem-based learning, group discussions, concept mapping, task-focussed workshops, hands-on experience, and reflective learning.

You will be assessed through a variety of both formative and summative approaches including case study report, individual video presentation, group presentation/pitch, grant proposal, reflective summary (group and individual), science communication, and a final research project developed in collaboration with UCL academics and/or with a programme partner.

Approximately 300 contact hours with approximately 1200 hours of self-directed learning.

Approximately 8-12 contact hours a week during term time, 35-50 hours per week total study time (including self-study). The contact time may rise to 30-35 hours per week during full-time project work with the self-study time reducing accordingly.

Modules

In term one you will take two core modules that cover (i) the fundamental aspects of ecological theory, survey design and sampling, sensing and sensor technology and their ecological applications; (ii) experimental design, field techniques, data collection, visualisation, management, and analysis. 

You will also be able to develop your interdisciplinary skills by choosing one optional module, a selection of modules will be available including one option to specialise in Citizen Science.

In term two, you will develop and integrate applied skills across a range of activities delivered from three modules. You will explore the use, design, and deployment of different sensing systems and become familiar with different types of hardware and their modalities. 

Then you will progress your computational and analytical skills using environmental data, including that collected from the sensors you have deployed in the Queen Elizabeth Olympic Park, and apply different techniques and analyses to explore, understand, and develop insights from your results. 

You will also take one more optional module from a selection, which will again include one option to specialise in Citizen Science. 

Finally, you will bring your hardware and analytical skills together as you work in teams to address a real-world biodiversity and/or environmental problem and develop its solution.

Term three is dedicated to your research project. With academic support from experts in the field, you will develop an original question with UCL academics and/or in collaboration with industry partners, which in order to answer, you will draw upon your learning and experience from terms one and two. 

Depending on the project, it may be possible to develop your research question directly from the work you complete on the Nature-Smart Challenge module.

Furthermore, weekly tutor-facilitated, student-led workshops will provide the platform for you to discuss the central themes in conducting research across ecology and data science topics, and explore and resolve challenges faced in your own research-project work in collaboration with your peers.

If your research project is fully or jointly supervised by a partner you will be expected to spend research time at partner premises (e.g. NHM is located in South Kensington, ZSL is located in Regent’s Park). 

Similarly, if your research project is supervised by a member of academic staff based at Bloomsbury, you will need to spend time at this campus.

Year one:
In term one you will take one core module (Foundations in Ecology and Ecological Monitoring), that covers the fundamental aspects of ecological theory, survey design and sampling, sensing and sensor technology and their ecological applications. 

You will also be able to develop your interdisciplinary skills by choosing one optional module (Foundations of Citizen Science or, subject to timetabling, a Bloomsbury-based module (Biodiversity Generation and Maintenance; Behavioural Ecology for the Anthropocene or Science Communications for Biologists).

In term, two you will take one core module (Technology for Nature) in which you will develop applied skills and explore the use, design, and deployment of different sensing systems and become familiar with different types of hardware and their modalities as you deploy sensors in the Queen Elizabeth Olympic Park. You will also choose from another set of option modules, including Designing and Managing Citizen Science.

Term three is dedicated to your research project. With academic support from experts in the field, you will develop an original question with UCL academics and/or in collaboration with industry partners, which in order to answer, you will draw upon your learning and experience from terms one and two and attend a series of tutor-facilitated, student-led workshops. 

These will provide the platform for you to discuss the central themes in conducting research across ecology and data science topics, and explore and resolve challenges faced in your own research-project work in collaboration with your peers.

Year two:
In term one you will take one core module, Computational Methods in Biodiversity Research, that will introduce you to experimental design, field techniques, data collection, visualisation, management, and analysis.

In term two, you will develop and integrate applied skills from Year one’s ‘Technology for Nature’ module across a range of activities delivered from two further modules (AI for the Environment,  Nature-Smart Challenge). 

You will first in ‘Al for the Environment’ progress your computational and analytical skills using environmental data, including that collected from sensors deployed in the Queen Elizabeth Olympic Park, and apply different techniques and analyses to explore, understand, and develop insights from your results. 

Finally, in ‘Nature-Smart Challenge’, you will bring your hardware and analytical skills together as you work in teams to address a real-world biodiversity and/or environmental problem and develop its solution.

In term three, you will complete Nature-Smart Challenge and will continue with your research project. With academic support, you will further explore your original question with UCL academics and/or in collaboration with industry partners, drawing upon your learning and experience from year’s one and two. 

A further selection of tutor-facilitated, student-led workshops will provide the platform for you to discuss, explore and resolve challenges faced in your own research-project work in collaboration with your peers.

For both years one and two, if your research project is fully or jointly supervised by a partner you will be expected to spend research time at partner premises (e.g. NHM is located in South Kensington, ZSL is located in Regent’s Park). Similarly, if your research project is supervised by a member of academic staff based at Bloomsbury, you will need to spend time at this campus.

Compulsory modules








Optional modules








Please note that the list of modules given here is indicative. This information is published a long time in advance of enrolment and module content and availability are subject to change. Modules that are in use for the current academic year are linked for further information. Where no link is present, further information is not yet available.

Students undertake modules to the value of 180 credits in total. Upon successful completion of 180 credits, you will be awarded an MSc in Ecology and Data Science.

Fieldwork

There is a one-week fieldwork course in term one which takes place in the Queen Elizabeth Olympic Park where the UCL East campus is based. You will be spending a lot of time outdoors, so please be prepared with suitable weather-proof clothing, sturdy shoes, and Wellington boots, if you have them.

Accessibility

Details of the accessibility of UCL buildings can be obtained from AccessAble. Further information can also be obtained from the UCL Student Support and Wellbeing Services team.

Fees and funding

Fees for this course

UK students International students
Fee description Full-time Part-time
Tuition fees (2025/26) £24,100 £12,050
Tuition fees (2025/26) £39,800 £19,900

The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Where the programme is offered on a flexible/modular basis, fees are charged pro-rata to the appropriate full-time Master's fee taken in an academic session. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Students website: ucl.ac.uk/students/fees.

Additional costs

For Full-time and Part-time offer holders a fee deposit will be charged at 10% of the first year fee.

Further information can be found in the Tuition fee deposits section on this page: Tuition fees.

Students are required to have a laptop which is suitable for running R software.

Depending on which options you select or where you do your project, you may need to travel to Bloomsbury, South Kensington, or Regent's Park. 

During the fieldwork at UCL East, you will be spending a lot of time outdoors, so please be prepared with suitable weather-proof clothing, sturdy shoes, and Wellington boots, if you have them. 

UCL’s main teaching locations are in zones 1 (Bloomsbury) and zones 2/3 (UCL East). The cost of a monthly 18+ Oyster travel card for zones 1-2 is £114.50. This price was published by TfL in 2024. For more information on additional costs for prospective students and the cost of living in London, please view our estimated cost of essential expenditure at UCL's cost of living guide.

Funding your studies

UCL East Scholarship

The scholarship works to support the ambitions of east Londoners by funding the fees and living costs of eligible Master's programmes including this MSc at UCL. Further details at: https://www.ucl.ac.uk/scholarships/ucl-east-london-scholarship.

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website.

See all the ways you can fund your studies

UCL East London Scholarship

Deadline: 26 June 2025
Value: Tuition fees plus £16,000 stipend ()
Criteria Based on financial need
Eligibility: UK

Next steps

Students are advised to apply as early as possible due to competition for places. Those applying for scholarship funding (particularly overseas applicants) should take note of application deadlines.

There is an application processing fee for this programme of £90 for online applications. Further information can be found at Application fees.

When we assess your application we would like to learn:

  • why you want to study Ecology and Data Science at graduate level
  • why you want to study Ecology and Data Science at UCL
  • what particularly attracts you to this programme
  • how your academic, professional and personal background meets the demands of this programme
  • where you would like to go professionally with your degree.

Together with essential academic requirements, the personal statement is your opportunity to illustrate whether your reasons for applying to this programme match what the programme will deliver.

Please note that you may submit applications for a maximum of two graduate programmes (or one application for the Law LLM) in any application cycle.

Choose your programme

Please read the Application Guidance before proceeding with your application.

Year of entry: 2025-2026

Apply for this course

Got questions? Get in touch

Division of Biosciences
Division of Biosciences

biosciences.ucleast@ucl.ac.uk

UCL is regulated by the Office for Students.