UCL Division of Biosciences


MSc Ecology and Data Science

This new MSc, the first of its kind globally, provides students with the interdisciplinary skills, theoretical and practical expertise needed to apply cutting-edge innovations in data science, citizen science, sensor technologies, and applied artificial intelligence to monitor and manage ecosystems and wildlife populations to understand and reverse catastrophic changes in global biodiversity. This interdisciplinary programme will develop the next generation of professionals better equipped to address critical global environmental challenges across society.
Programme Details 
Start DateSeptember entry
Awards AvailableMSc

1 year Full Time
2 years Part Time

LocationUCL East Campus

Why Study MSc Ecology and Data Science

Biodiversity and ecosystems underpin all human wellbeing and endeavours – from health and happiness to prosperity and security. Yet biodiversity is declining rapidly, with global and local extinctions, and widespread population declines. Meanwhile, land is increasingly under pressure to meet multiple requirements, including the production of sustainable energy, clean water, and healthy, sustainable food. The combined impacts of a growing human population, increasing production and consumption, and global climate change, present an enormous challenge for the management of natural resources and ecosystems.


High-resolution data streams produced from a growing array of sensor technologies such as high-resolution satellite imagery, visual and audio sensor data, geospatial tracking devices and environmental DNA, are revolutionizing how environments are monitored. Additionally, new advances in artificial intelligence and other statistical modelling tools are transforming our ability to analyze these high-resolution data, potentially transforming our understanding of how to manage ecosystems and meet the world’s critical global challenges. However, there is a knowledge and skills gap between ecology and state-of-the-art approaches in data science, sensor technologies and applied artificial intelligence that needs to be bridged to realize this potential.

The new MSc in Ecology & Data Science directly addresses this knowledge and skills gap, providing students with a unique and highly sought after expertise, attuned to addressing the critical ecological and environmental global challenges of our time. When making your application to this course, search for it on the system by using the word Ecology. This will bring up the relevant course options on the application system. 


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Why study this programme at UCL?


  • The MSc Ecology & Data Science is led by Dr Daniel Maynard, an Associate Professor in Quantitative Ecology at UCL. Daniel's research lies at the intersection of community ecology and macroecology, using a broad array of experimental and statistical approaches to understand how biodiversity is maintained in hyper-diverse ecosystems.
  • The MSc Ecology & Data Science is taught in UCL’s purpose-built People and Nature Lab at the new UCL East campus in the Queen Elizabeth Olympic Park in Stratford in East London.





  • Our People & Nature Lab represents an exciting new cross-disciplinary research and teaching partnership to facilitate innovative approaches to tackle the challenges posed by biodiversity loss, global ecosystem degradation and climate change, to support a more sustainable relationship between people and nature.
  • UCL’s breadth of expertise across disciplines, the scale of the investment in the new campus combine to create a unique opportunity to take our understanding of the interdependencies of the modern world and its impact upon our environment to the next level. The MSc will realise the vision of a new type of programme which addresses the urgent need to produce professionals with expertise in both ecology and data science, and will be taught by a cross-disciplinary team of scientists, including from UCL’s departments of GEE, Computer Science; Geography; Civil, Environmental and Geomatic Engineering; and The Bartlett Faculty of the Built Environment and industry partners from the Zoological Society of London and the Natural History Museum.
  • UCL’s People and Nature Lab expands the work of the Centre for Biodiversity and Environment Research (CBER) within the Research Department of Genetics, Evolution and Environment. Building on nearly two centuries of the study of the natural environment, CBER was established in 2013 as a world-leading centre of excellence for the study of the impact of rapid environmental change on biodiversity, how species are adapting to anthropogenic change, and how the degradation of nature impacts people and society.


Career opportunities

  • The sustainable management of environmental resources for expanding human populations, and how global climate change and biodiversity targets will be met, are crucial societal challenges of our time. Professionals who can design, implement, and measure the effectiveness of potential solutions to environmental challenges are in demand across a wide range of industries, from sectors such as the built environment, natural environment, and agriculture, to conservation and global policy. This creates a wealth of opportunities for individuals with ecological and environmental knowledge, who can use and apply a wide range of tools in data science and artificial intelligence to address these problems.
  • Graduates of the MSc Ecology and Data Science will leave with the project management skills, and theoretical and practical experience needed to implement cutting-edge statistical and computational solutions to address ecological and environmental challenges across society. This in-depth knowledge and experiential skill set will provide you with a unique point of difference that meets a fast-growing need across all industries.

Course content

The MSc programme consists of five compulsory modules, one optional module and a research project. 

Compulsory Modules

Foundations in Ecology and Ecological Monitoring


Learn key concepts in ecological theory and methods in environmental and biodiversity monitoring.

Computational Methods in Biodiversity Research


Develop the fundamental skills you will need to collect, manipulate, visualise and analyse environmental, biodiversity, and citizen science data, using open-source analytical and computational tools.

Technology for Nature


Explore the use, design, deployment and practicalities of different sensor systems for observing and monitoring wildlife populations.

AI for the Environment


Develop advanced analytical skills need to infer and model environmental and biodiversity data, using machine learning and other approaches

Nature-Smart Challenge


Implement new analytical skills within a group dynamic to address a real-world problem partnering with UCL academics or external industry partners.


Optional Modules

Foundations of Citizen Science

salton sea national wildlife

Foundation knowledge on citizen science and crowdsourcing, covering the theoretical roots of citizen science to its modern application across society and scientific fields

Biodiversity Generation and Maintenance


Foundational knowledge on the generation and maintenance of biodiversity, covering topics including phylogenetics, macroecology and biodiversity gradients, and assessment of extinction risk.

Behavioural Ecology for the Anthropocene

foxes in the city 3
This module unites the classical study of animal behaviour, using Niko Tinbergen’s four ‘whys’ of behaviour as a framework, with theory on the role of phenotypic plasticity in changing environments.

Science Communication for Biologists

A practical introduction to effectively communicating scientific research to different audiences ranging from your scientific peers, policymakers, the media and the general public.



MSc Ecology and Data Science Research Project

A range of projects will be available working with UCL academics and programme partners including the Zoological Society of London, and the Natural History Museum London. Students will carry out an original piece of research that answers an ecological and data science question developed with the student’s expert supervisory team. The student will produce a dissertation detailing and critically analysing their work, and will be expected to prepare an oral presentation delivered to an invited audience.

Facilities and fieldwork

  • Students on the MSc programmes are taught in the new facilities in the People and Nature Lab at the UCL East campus

  • These facilities include cutting-edge laboratories and workshops, and the ‘Living Laboratory’ of the Queen Elizabeth Olympic Park (QEOP) designed to be home to both people and nature. Networks of sensors in the QEOP collect environmental and biodiversity data and students would have the chance to build and deploy new sensors and analyse the existing data streams. 



As well as researchers across UCL departments, students on this programme have the opportunity to work on a research project with academics at The Institute of Zoology Zoological Society of London and the Natural History Museum.

People and Nature Lab's First Project

nyctalus leisleri

Whilst the new UCL East campus was being built, the People and Nature Lab worked with colleagues at Intel to develop the world’s first automated smart detectors for monitoring bats through their echolocation calls. Our ‘Echo Boxes’ continuously record and identify bat calls using machine learning across the Queen Elizabeth Olympic Park sending the results back in real-time to understand the health of the environment.

Find out more about the Project

Research Labs at UCL in relevant fields

Students on this programme may have the opportunity to work with one or more of UCL's world-leading research laboratories through the programme or as part of their research project. These are some of the UCL labs across faculties that are relevant to the content taught within this programme.

Green Parrot

UCL Aliens Lab

Prof Tim Blackburn

Bee on flower

Global Biodiversity Change

Dr Tim Newbold

jelly fish image

Sumner Lab

Prof Seirian Sumner

Orangutan holding bananas

Day Lab

Prof Julia Day

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