Environmental Data Science and Analytics MSc

Year of entry

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Start date
September 2024
Delivery type
On campus
12 months full time
Entry requirements
A bachelor degree with a 2:1 (hons)
Full entry requirements
English language requirements
IELTS 6.5 overall, with no less than 6.0 in any component
UK fees
£13,500 (Total)
International fees
£30,750 (Total)

Course overview

Environmental Data Science and Analytics MSc banner image

As global discussions are increasingly focused on the environment and sustainability, the need to understand and interpret both built and natural environmental data is more crucial than ever. Governments, organisations, and industries across the world are seeking individuals who can leverage data to make informed, sustainable decisions.

Our Environmental Data Science and Analytics MSc is designed to give you a fuller understanding of data and what appropriate interpretation and insight generation means in various environmental contexts. Throughout, you'll develop a broad understanding of environmental issues and how data driven analysis can help address these.

You’ll build the skills needed to analyse, understand, interpret and visualise complex environmental data; generating insights that address real-world environmental challenges. You’ll apply techniques from data science including environmental modelling, machine learning, visualisation and data curation to environmental data, with a focus on analysis through programming.

Here at Leeds, you’ll have the opportunity to learn from leading researchers in the field, with input from the School of Geography, the School of Earth and Environment, and the School of Computing.

By the end of the course, you’ll be equipped with the specialist knowledge and skills that are sought after across multiple industries, especially those dealing with large scale environmental data to address the needs of society.

Why study at Leeds:

  • Our globally-renowned research from the School of Geography, School of Earth and Environment and the Leeds Institute for Data Analytics, the national institute for data science and artificial intelligence feeds into the course, shaping your learning with the latest thinking in environmental data science and analytics.
  • Advance your skills and knowledge in areas such as environmental policies, programming for data science, machine learning, AI, satellite/UAV technology and interpreting large hyperdimensional data.
  • Tailor the course to suit your career aspirations with a selection of optional modules in areas like web-based GIS, GIS and environment and digital image processing for environmental remote sensing.
  • Conduct your own project work which will enable you to develop transferable skills as a researcher, investigating a real-world issue that explores and develops your interests.
  • Access specialist facilities which will complement your learning including laboratories and an excellent GIS computer cluster with industry-standard software.
  • Experience expert theoretical and practical teaching delivered by a programme team of academics who are actively engaged in ground-breaking research in the School of Geography and the School of Earth and Environment.
  • Put theory into practice with exciting fieldwork opportunities, allowing you to develop the understanding and skills in data acquisition and deployment of sensors to collect environmental data.

Course details

The course is designed to give you an understanding of the end-to-end workflow of data in an environmental context, allowing the nuances of how and why data from the environment is collected, and the need for appropriate analysis for policy and management relevant insights.

The course has a particular focus on the data science and analytic techniques, giving you the skills to apply them to complex environmental issues – allowing you to gain in-depth specialist knowledge needed to ensure that data-driven environmental decisions are based on accurate, reliable and correctly interpreted data. A multitude of environmental data, and its specific context and form, is considered and worked with to give a breadth of experience necessary to derive insights and solutions creatively to real world problems.

You'll take compulsory modules that are designed to progressively include more programming and data science, moving from fundamental knowledge to application of skills in a creative manner.

On completion of the course, you'll be able to demonstrate in-depth data science techniques to environmental data, with understanding of the data context allowing for sophisticated insight generation that is appropriate to inform decision making in multiple scenarios. The combination of relevant data science techniques to environmental data and understanding of environmental science context will allow you to demonstrate an ability to generate creative solutions, alongside insights.

Project work

The course culminates with a 60-credit final project, that is designed to reflect the types of outputs you might encounter in their future careers, a code workbook and practical briefing report. This brings together skills on data science and programming, and meaningful insight generation that can be used to inform decision making in multiple contexts, including influencing policy.


Fieldwork provides a great opportunity to study a fascinating subject in contrasting environments away from the University. On this course, we offer the opportunity to undertake fieldwork as part of the Data to Insights in Multiple Environments module, in the UK, to develop your understanding and skills in data acquisition and deployment of sensors to collect environmental data. In addition, you can also opt to conduct fieldwork as part of your dissertation. An increased understanding of how and why data is collected will allow for enhanced context appropriate interpretation of analysis that results from data.

Course Structure

The list shown below represents typical modules/components studied and may change from time to time. Read more in our terms and conditions.

Most courses consist of compulsory and optional modules. There may be some optional modules omitted below. This is because they are currently being refreshed to make sure students have the best possible experience. Before you enter each year, full details of all modules for that year will be provided.

For more information and a full list of typical modules available on this course, please read Environmental Data Science and Analytics MSc in the course catalogue


Compulsory modules

Programming for Data Science – 15 credits

This module is designed to give those with little or no programming experience a firm foundation in programming for data analysis and AI systems, recognising a diversity of backgrounds. The module will also fully stretch those with substantial prior programming experience (e.g., computer scientists) to extend their programming and system-building knowledge through self-learning supported by on-line courseware.

Data Science for Practical Applications – 15 credits

You’ll learn the foundations in data science training, being introduced to concepts in data handling, exploratory data analysis, machine learning and visualisation. You'll have opportunities to work with a variety of spatial and spatiotemporal datasets relating to practical problems in the real world, including multiple environments.

Creative Coding for Real World Problems – 15 credits

This will be your chance to enhance your creativity and develop your technical skills in relation to variety of mixed environments. You’ll work in groups to derive solutions that are novel and insightful through application of programming skills.

Data to Insights in Multiple Environments – 30 credits

Here, we’ll bring together a myriad of leading researchers working in different environments (e.g. terrestrial, fresh water, and marine) to first provide you with an environmental content of why and how data is collected. You’ll then analyse and draw insights in practical sessions to explore how this data can be analysed for insight generation in a meaningful and appropriate way. This module incorporates a field course, where you'll get hands-on with modular sensors and deployment.

Machine Learning for Environmental Data – 15 credits

Focusing on how techniques from machine learning can be utilised for a context specific analysis for large scale problems, you’ll learn the fundamentals of machine learning and the applications across multiple data types, including spatial data.

Environmental Data Science Project – 60 credits

This project will take the place of the traditional dissertation. Your project submission will incorporate a data science output, in the form of a ‘notebook’-style analysis, and an associated written report, which outlines prior work and context, the analysis process, the results, and associated policy or practical implications.

Optional modules

Environmental Assessment – 15 credits

GIS and Environment – 15 credits

Digital Image Processing for Environmental Remote Sensing – 15 credits

Web-based GIS – 15 credits

Learning and teaching

The course is designed to develop both your practical skills in data science and develop deeper understanding of environmental issues and contexts from which data is derived. This course takes a problem-based learning approach – presenting real world problems where you must use your growing skills and knowledge to develop creative solutions. This is embodied using practical sessions, individual and group, where you’ll practice skills; minimising the number of modules that are purely lecture based. Datasets used are sourced from leading researchers in the field, or from industry, reflecting a variety of data types and contexts.

A combination of face-to-face learning in a variety of learning environments such as practical labs, lectures, seminars, and group working are used alongside digital platforms to enhance and scaffold learning.

Active research environment

You'll be taught by an experienced team of academics and researchers who are actively engaged in cutting-edge research and part of the Centre for Spatial Analysis and Policy, River Basin Processes and Management, and Ecology and Global Change research groups within the School of Geography, School of Earth and Environment..

Specialist facilities

You'll have access to excellent teaching facilities within the School of Geography, including a GIS computer cluster with industry-standard software.

Programme team

The Programme Leader, Dr Arjan Gosal, is a Lecturer in Socio-Environmental Data Science. His research focuses on the use of machine learning techniques applied to real-world environmental problems, with a particular interest in the importance of local context in the role of management of large-scale nature areas.

On this course you’ll be taught by our expert academics, from lecturers through to professors. You may also be taught by industry professionals with years of experience, as well as trained postgraduate researchers, connecting you to some of the brightest minds on campus.


The course is designed to assess you through the application of your data science skills within the context of environment problems; namely this is through the writing of code and analysis and interpretation of findings, with a focus on insight generation. To replicate the type of work graduates from this course are expected to encounter, assessments will be both individual and group based, with the interpretation taking forms such as briefing documents, panel presentations, and executive summaries.

Assessments will capture your ability to conduct in-depth enquiries using data science, within the context of complex environment problems and challenges.


Entry requirements

A bachelor degree with a 2:1 (hons) in a relevant subject. Those with a background in geography, environmental science, natural sciences, engineering, or computer science, or who can demonstrate good quantitative and/or computing skills, are particularly encouraged to apply.

Applicants who do not meet the above requirement but can demonstrate several years of professional experience in a relevant field will also be considered. In this case, please contact the Admissions Team to discuss your suitability for the programme.

Applicants should possess a keen interest in harnessing data science to understand the natural environment and address environmental issues.

We accept a range of international equivalent qualifications. For more information, please contact the Admissions Team.

English language requirements

IELTS 6.5 overall, with no less than 6.0 in any component. For other English qualifications, read English language equivalent qualifications.

Improve your English

International students who do not meet the English language requirements for this programme may be able to study our postgraduate pre-sessional English course, to help improve your English language level.

This pre-sessional course is designed with a progression route to your degree programme and you’ll learn academic English in the context of your subject area. To find out more, read Language for Science (6 weeks) and Language for Science: General Science (10 weeks)

We also offer online pre-sessionals alongside our on-campus pre-sessionals. Find out more about our six week online pre-sessional.

You can also study pre-sessionals for longer periods – read about our postgraduate pre-sessional English courses.

How to apply

Application deadlines

We operate a staged admissions process for this course with selection deadlines throughout the year.

31 July 2024 – International applicants

8 September 2024 – UK applicants

Please read our How to Apply page for full details, including application deadlines and what to include with your application.

Click below to access the University’s online application system and find out more about the application process.

If you're still unsure about the application process, contact the admissions team for help.

Read about visas, immigration and other information in International students. We recommend that international students apply as early as possible to ensure that they have time to apply for their visa.

Admissions policy

University of Leeds Admissions Policy 2025

This course is taught by

School of Geography

Contact us

School of Geography Postgraduate Admissions Team

Email: geo-tpg-enq@leeds.ac.uk


UK: £13,500 (Total)

International: £30,750 (Total)

Read more about paying fees and charges.

For fees information for international taught postgraduate students, read Masters fees.

Additional cost information

There may be additional costs related to your course or programme of study, or related to being a student at the University of Leeds. Read more on our living costs and budgeting page.

Scholarships and financial support

If you have the talent and drive, we want you to be able to study with us, whatever your financial circumstances. There may be help for students in the form of loans and non-repayable grants from the University and from the government.  Find out more at Masters funding overview.

Career opportunities

The specific environmental context of the course allows you to enter specialised careers that require a contextual understanding of data and context from the environmental domain, including environmental consultancies, transport, engineering, utilities (e.g. water, energy, renewables) and government agencies.

More widely, the course will equip you with data science and analytical skills that are vital to many industries across the globe. Growth in the data science field is only increasing, with the number of opportunities growing year on year, from positions in local authorities and governments, industry (e.g. finance, telecoms), NGOs; wherever there is a need for data scientist or data analyst.

Plus, University of Leeds students are among the top 5 most targeted by top employers according to The Graduate Market 2024, High Fliers Research, meaning our graduates are highly sought after by some of the most reputable companies in the field.

Careers support

At Leeds, we help you to prepare for your future from day one — that’s one of the reasons Leeds graduates are so sought after by employers. The University’s Careers Centre is one of the largest in the country, providing a wide range of resources to ensure you are prepared to take your next steps after graduation and get you where you want to be.

  • Dedicated Employability Officer — gain quality advice, guidance and information to help you choose a career path. From CV and cover letter writing to supporting you with job applications, our School’s dedicated Employability Enhancement Officer is on hand to help maximise your capabilities through a process of personal development and career planning.
  • Employability and networking events — we run a full range of events, including careers fairs in specialist areas and across broader industries, with employers who are actively recruiting for roles, giving you the opportunity to network with industry sponsors. 
  • MyCareer system — on your course and after you graduate, you’ll have access to a dedicated careers portal where you can book appointments with our team, get information on careers and see job vacancies and upcoming events.
  • Opportunities at Leeds — there are plenty of exciting opportunities offered by our Leeds University Union, including volunteering and over 300 clubs and societies to get involved in.

Find out more at the Careers website.