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Ecology and Data Science MSc

London, Stratford (UCL East)

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. The new MSc in Ecology & Data Science will directly address the 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.

UK students International students

Study mode

UK tuition fees (2022/23)

£19,400

£9,700

Overseas tuition fees (2022/23)

£32,100

£16,050

Duration

1 calendar year

2 calendar years

Programme starts

September 2022

Applications accepted

All applicants: 18 Oct 2021 – 31 Mar 2022

Applications open

Apply for this course

We recommend that you submit your application as soon as possible. The programme may remain open if places are still available after 31 March 2022 and will be closed as soon as it is full or by 30 June 2022.

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.

English language requirements

If your education has not been conducted in the English language, you will be expected to demonstrate evidence of an adequate level of English proficiency.

The English language level for this programme is: Good

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. International Preparation Courses

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

The MSc Ecology and Data Science will develop your data science skills from the conceptual to the practical following a unique approach that builds a foundational understanding of ecological theory, biodiversity monitoring methods, statistical programming, practical knowledge of cutting-edge sensor technologies, and environmental applications of artificial intelligence and data-driven modeling.

Who this course is for

Our new cross-disciplinary postgraduate programme will welcome students interested in ecology and wildlife conservation technologies, citizen science to big data analytics, sustainable urban design and health.

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

What this course will give you

Facing the twin global challenges of the climate and biodiversity crises, we urgently need to understand how our ecological systems and environmental resources can better be managed as they underpin all human wellbeing and endeavours, from health and happiness to prosperity and security. This programme will develop the next generation of professionals better equipped to address critical global environmental challenges across society.

Read about what you'll get out of a graduate programme

The foundation of your career

Employability

Upon completion of the MSc Ecology & Data Science, you will possess the project management skills, and theoretical and practical experience needed to implement cutting-edge technological, statistical, and computational solutions to address ecological and environmental challenges across society. You will also leave with the necessary insight to plan and undertake independent research, and the ability to report its findings to a variety of audiences.

Employment destinations may include engineering, development and environmental consultancies; academia; museums; civil service; local government; environmental charities; conservation charities.

Teaching and learning

You will be assessed through a variety of both formative and summative approaches including case study report, individual video presentation, group presentation/pitch, open book exam, 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.

Modules

Full-time

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 (BIOS0035: Foundations of Citizen Science, or BIOS0036: Social Prescribing and Community Wellbeing). Alternatively, it may be possible to select a Bloomsbury-based module (BIOS0027: Biodiversity Generation and Maintenance; BIOL0048: Behavioural Ecology for the Anthropocene; BIOS0021: Science Communication for Biologists) but availability will be subject to timetabling.

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. 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 module BIOS0033: Nature-Smart Challenge. 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 = South Kensington, ZSL = 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.

Part-time

Year One:
In term one you will take one core module (BIOS0003: 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 develop your interdisciplinary skills by choosing one optional module (BIOS0035: Foundations of Citizen Science, or BIOS0036: Social Prescribing and Community Wellbeing). Alternatively, it may be possible to select a Bloomsbury-based module (BIOS0027: Biodiversity Generation and Maintenance; BIOL0048: Behavioural Ecology for the Anthropocene; BIOS0021: Science Communication for Biologists) but availability will be subject to timetabling.

In term two you will take one core module (BIOS0031: 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.

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 that 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, BIOS0002: 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 Yr 1’s ‘Technology for Nature’ module across a range of activities delivered from two further modules (BIOS0032: AI for the Environment, BIOS0033: 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.

Term three is again dedicated to 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 = South Kensington, ZSL = 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

Computational Methods in Biodiversity Research

Foundations in Ecology and Ecological Monitoring

Technology for Nature

AI for the Environment

Nature-Smart Challenge

MSc Ecology and Data Science Research Project

Optional modules

Behavioural Ecology for the Anthropocene

Science Communications for Biologists

Biodiversity Generation and Maintenance

Foundations of Citizen Science

Social Prescribing and Community Wellbeing

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 is subject to change.

Upon successful completion of 180 credits, you will be awarded an MSc in Ecology and Data Science.

Accessibility

Details of the accessibility of UCL buildings can be obtained from AccessAble accessable.co.uk. Further information can also be obtained from the UCL Student Support & Wellbeing team.

Visit us

All open days and events

30 November 2021 — 9 December 2021

Online - Open day

Graduate Open Events: Applying for Graduate Study at UCL

Fees and funding

Fees for this course

UK students International students

Fee description	Full-time	Part-time
Tuition fees (2022/23)	£19,400	£9,700
Tuition fees (2022/23)	£32,100	£16,050

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

None

For more information on additional costs for prospective students please go to our estimated cost of essential expenditure at Accommodation and living costs.

Funding your studies

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

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 and £115 for paper 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 in any application cycle.

This programme requires two references. Further information regarding references can be found in our How to apply section.