Environmental Sustainability and Data Science MSc
London, United Kingdom
MSc
DURATION
1 year
LANGUAGES
English
PACE
Full time
APPLICATION DEADLINE
EARLIEST START DATE
Sep 2026
TUITION FEES
GBP 18,000
STUDY FORMAT
On-Campus
Book to attend an Online Open Day to find out more - Undergraduate event on March 21 and Master's event on March 4.
In the current climate emergency, the need for innovative, data-driven solutions has never been greater. Our Environmental Sustainability and Data Science MSc is a multidisciplinary, forward-thinking course that prepares you to tackle global issues by combining the power of technology and data science with the principles of environmental sustainability.
This course will equip you with the knowledge and skills to address environmental challenges through the development and application of new technologies, processes, and solutions derived from biological systems and the use of data-driven decision-making tools. You’ll develop expertise in data science techniques, including programming in R and Python, machine learning and big data analysis, to interpret and manage complex environmental datasets and develop sustainable solutions.
You’ll gain a deep understanding of sustainability frameworks, circular economy principles, policy and research skills and learn from real-world case studies. Fieldwork and lab sessions will equip you with hands-on experience of monitoring and interpreting environmental data, allowing you to assess the impact of human activities on ecosystems and make informed, sustainable decisions. You’ll apply your knowledge practically through collaborative projects, engaging with local communities and industry experts to co-create solutions and make a positive impact whilst gaining valuable work experience.
This MSc offers a unique opportunity to bridge the gap between data science and environmental sustainability, empowering you to become a leader in the development of solutions that will shape the future of our planet.
Top reasons to study with us
- Engage with communities and industry experts – you’ll benefit from our strong relationships with local community and business groups such as the Fitzrovia Partnership and gain valuable insights from guest speakers from industry, government and research institutions
- Hands-on learning and practical experience – you'll learn through diverse methods including practical workshops, fieldwork and lab sessions and experience sustainability initiatives first-hand through field trips, allowing you to draw inspiration from real-world examples
- Master data science for sustainability – this course will introduce you to the fundamentals of computing for data science, including the most prominently used programming languages, R and Python, equipping you with the skills to develop sustainable solutions
- Tackle real-world environmental challenges – our unique blend of environmental sustainability and data science will equip you with the in-demand skills needed to become an environmental leader and solve complex environmental problems
This course adopts a comprehensive learning strategy that emphasises independent research, study and active engagement with key stakeholders. Our approach combines a range of diverse teaching methods including practical workshops, fieldwork and laboratory sessions, lectures and tutorials, group work and presentations and poster sessions.
The following modules are indicative of what you will study on this course.
Please note that option module availability can be limited due to factors like timetabling and space constraints, so your first choice is not always guaranteed.
Core modules
- Environmental Sustainability Challenges
- Introduction to Data Science - Fundamentals of Programming
- Bioinnovation Laboratory
- Sustainable Solutions with Machine Learning and Big Data
- Environmental Policy, Assessment and Climate Change
- Postgraduate Research Methods
- Postgraduate Project
Option modules
- Communicating Science
- Data Visualisation and Dashboarding
- Science, Technology and Commercialisation
- Sustainable Biotechnology
In today’s climate emergency, governments, organisations, and industries worldwide are increasingly seeking professionals capable of using data to make informed, sustainable decisions to address contemporary global challenges. This growing demand has led to a significant rise in the need for environmental scientists with data science expertise.
Graduates of this course will be uniquely positioned for a diverse range of employment opportunities, using your interdisciplinary expertise in environmental science, data science, and innovative problem-solving.
Industry links
The course will provide you with opportunities to gain industry insights and exposure to real-world perspectives. You’ll hear from guest speakers from various sectors, including industry, governmental agencies, community organisations, charities, and research institutions (e.g. the Department for Environment, Food and Rural Affairs (DEFRA), Westminster City Council, La Loma Viva, Daiichi Sanko, Merck). Not only will this provide you with valuable insights, but also the opportunity to build your professional network before graduation.
Graduate employers
Graduates from this course will be prepared to work at organisations such as:
- Charities and NGOs
- Corporate Social Responsibility/Sustainable Development departments
- Environmental agencies
- Local government
- Research Institutions and Universities
- Renewable Energy Companies
- Sustainability and Environmental consultant firms
- Technology and Data companies
- Waste Management companies
Job roles
This course will prepare you for roles in a variety of areas, including:
- Community Sustainability Coordinator
- Environmental consultant
- Environmental Data Scientist
- Environmental Educator or Outreach Coordinator
- Environmental Policy Analyst
- PhD researcher/Research Scientist
- Product Sustainability Manager
- Research Scientist
Below you will find how learning time and assessment types are distributed on this course. The graphs below give an indication of what you can expect through approximate percentages, taken either from the experience of previous cohorts, or based on the standard module diet where historic course data is unavailable. Changes to the division of learning time and assessment may be made in response to feedback and in accordance with our terms and conditions.
How you’ll be taught
Teaching methods across all our postgraduate courses focus on active student learning through lectures, seminars, workshops, problem-based and blended learning, and where appropriate practical application. Learning typically falls into two broad categories:
- Scheduled hours: examples include lectures, seminars, practical classes, workshops, supervised time in a studio
- Independent study: non-scheduled time in which students are expected to study independently. This may include preparation for scheduled sessions, dissertation/final project research, follow-up work, wider reading or practice, completion of assessment tasks, or revision
How you’ll be assessed
Our postgraduate courses include a variety of assessments, which typically fall into three broad categories:
- Written exams: end of semester exams
- Practical: examples include presentations, podcasts, blogs
- Coursework: examples include essays, in-class tests, portfolios, dissertation