Key Information

We strive to increase and broaden inclusivity and support everyone, regardless of background, in breaking down any barriers to your application the Department.

If you are unable to pay a course’s application fee, we encourage you to apply for a fee waiver. If you are interested in this MSc and wish to apply for a fee waiver, please contact ese-msc-edsml@imperial.ac.uk prior to starting your application.

For the future of environmental sciences

Students in computational classJoin this multidisciplinary 1 year MSc programme to train in fundamental computational and data science skills and shape your understanding of environmental science.

The MSc Environmental Data Science and Machine Learning at Imperial College London will train students in fundamental computational and data science skills for application across the environmental sciences. The course is led by expert computational scientists in the Department of Earth Science and Engineering. The programme offers a focus on environmental big data in addition to established modules in machine learning, computational science and modern programming skills that run in the Applied Computational Science and Engineering MSc.

Find the most recent ‌MSc Environmental Data Science and Machine Learning Programme Specification on the Imperial College London course page for the MSc Environmental Data Science and Machine Learning.

Who is the course for?

If you:

  • have a first degree in an Engineering or Science-based subject and would like to expand your knowledge of Environmental Data Science, or
  • would like training in Data Science and Machine Learning with a strong applied focus, or
  • have a background in Environmental Science and want to develop computational/data science and coding skills,

then you will benefit from joining the MSc Environmental Data Science and Machine Learning.

Applicants are expected to have some experience with programming in a high-level language and a background in University-level mathematics. Please refer to these frequently asked questions for further information regarding prerequisite knowledge.

Why should I apply?

Are you interested in learning data science and machine learning skills and to apply them to environmental science topics including the low carbon energy transition, sustainability, future cities, water & air quality?

This programme will:

  • Educate future environmental scientists in the field of data science, machine learning, remote sensing, environmental monitoring, scientific programming and computational methods applied to environmental science and engineering.
  • Allow students to generate, manipulate, interrogate, analyse, visualise, interpret, invert and learn from data to explore a range of problems using a variety of techniques.
  • Teach underlying theory as well as the implementation of methods in high-quality code.
  • Expose students to a wide range of data science and machine learning applications.

Course Information

Study Programme

The study programme consists of eight taught modules, and one individual research project which accounts for one third of the study programme.

Term 1

Modern programming methods and Cloud Computing

Computational Mathematics

Environmental Data

Term 2

Applying Computational/Data Science (several short group projects)

Advanced programming

Big Data Analytics

Inversion and Optimisation

Term 3 (summer)

Machine Learning

Independent Research Project.

Some representative research project titles include:

  • Deep Learning applied to the interpretation of subsurface data
  • A GNSS Satellite Selection Scheme based on Line-of-Sight and Satellite Geometry with a Machine Learning Approach
  • A Machine Learning Approach to the Prediction of Tidal elevation
  • Applying Novel Data-Driven Techniques to Wind Turbine Predictive Maintenance
  • ARGO Trainer: Developing of a new software platform to annotate, visualize and analyze ARGO float data.
  • Assessing the environmental sentiments of the public using Twitter data
  • Automated crater detection based on the YOLOv3 architecture and its application to CTX imagery
  • Machine learning based bathymetry derivation from high-resolution satellite imagery
  • Machine Learning for Automatic Facies Classification from 3D Geophysical Models
  • Mapping coastal wetlands with Google Earth Engine and Machine Learning
  • Multi-scale tsunami inundation and sea defence modelling
  • Optimal Drone Recharging Scheduling for Wireless Sensor Power Transfer and Data Collection
  • The assessment and optimisation of CO2 storage in the UK for climate change mitigation
  • Machine Learning-based Classification of Europa’s Fractures

Students will have the chance to participate in individual and group research projects as well as to write reports and present technical work, developing the project management and numerical skills that are desired by employers.

You can see the teaching schedule represented visually below. If you would like an accessible version of this information, please contact ESE webmaster.

Courses shared by ACSE, EDSML, and GEMS (ESE MScs)
Courses shared by ACSE, EDSML, and GEMS (ESE MScs). Yellow and purple courses/project modules are shared across MScs
Careers

Graduates of this course will be well placed to fill the significant market demand for those with applied, hands-on computational and data science experience.

Many of the skills you learn are applicable broadly across all of science and engineering and so potential career paths are hugely diverse. The additional knowledge of environmental science and associated engineering solutions you will be exposed to in this course will make you particularly attractive to anything from relatively small environmental and engineering consultancies to large multi-national organisations including those in the energy and big tech sectors.

The skills gained on this course are also important in research and will be of value for jobs in R&D or future PhD studies. See for example previous skills gaps reviews in the Environment Sector.

Course Overview

This immersive, hands-on MSc course will enable students to develop their skills and techniques for a range of Environmental science and engineering applications utilising Cloud and High Performance Computing resources. Students will learn alongside world-class researchers in the Department of Earth Science and Engineering. There will be a strong emphasis on high productivity problem solving using modern computational and data science techniques.

Applicants who want to pursue highly numerate and computational based careers in environmental science and engineering, including climate, renewable energies, sustainability and earth imaging, are a target for this course. Graduates will develop the skills necessary to enter the modern industrial workforce. This MSc will also prepare for your PhD studies in fields such as computational and data science techniques, simulation, numerical modelling, optimisation and inversion, and machine learning applications.

The Environmental Data Science and Machine Learning MSc programme will ensure that students are able to apply appropriate computational techniques to understand, define and develop solutions to a range of environmental science and engineering problems. You will have the chance to participate in individual and group research projects as well as to write reports and present technical work, developing the project management and numerical skills desired by employers.

How to apply

We strive to increase and broaden inclusivity and support everyone, regardless of background, in breaking down any barriers to you applying to the Department. If you are unable to pay a course’s application fee, we encourage you to apply for a fee waiver. If you are interested in this MSc and wish to apply for a fee waiver, please contact ese-msc-edsml@imperial.ac.uk prior to starting your application.

Applications for EDSML are accepted online via the Imperial College London Postgraduate website. Find out more about how to apply.

Please note that even if you don’t have prior experience in all of the areas listed above, but think your background and skills are a good fit for the programme and are excited about developing your skills in data science and machine learning with a strong focus on environmental science, please don’t hesitate to contact the course administrator to find out more and discuss.

Scholarship opportunities

MSc Ada Lovelace Scholarship for Women

We have four departmental scholarships available to women who are offer holders for any of the following programs, for 2025 entry: 

·       MSc Geo-Energy with Machine Learning and Data Science [contact: Thomas Dray]

·       MSc Applied Computational Science and Engineering [contact: Ying Ashton]

·       MSc Environmental Data Science and Machine Learning [contact: Ying Ashton]

·       MSc Renewable Energy with AI and Data Science: Geology & Geophysics [contact: Sophie Pirouet]

To request the scholarship application form, please email the course administrator (name above). 

The scholarships will be awarded to women who demonstrate exceptional academic merit and/or potential and are open to Home and Overseas applicants (3 x Home and 1 x Overseas). The Scholarships will cover Home/Overseas Tuition fees and a stipend.  Additionally for those interested we provide Ada Lovelace PhD scholarship opportunities which will be available to apply for during your Masters year.

How to apply 

To be considered for a Scholarship for one of the above MSc programmes in academic year 2025-26, please apply for the course via the Imperial MSc graduate course admissions process by Friday 28thMarch 2025 , and complete the scholarship application form by 11am (UK time) on Friday 11th April 2025.

MSc Ada Lovelace Scholarship for Home Students

The department has four scholarships available to students who are offer holders for any of the following programmes, for 2025 entry: 

·       MSc Geo-Energy with Machine Learning and Data Science [contact: Thomas Dray]

·       MSc Applied Computational Science and Engineering [contact: Ying Ashton]

·       MSc Environmental Data Science and Machine Learning [contact: Ying Ashton]

·       MSc Renewable Energy with AI and Data Science: Geology & Geophysics [contact: Sophie Pirouet]

To request the scholarship application form, please email the course administrator (name above). 

Scholarships will provide full home tuition fees for each successful candidate and will be awarded to candidates who demonstrate exceptional academic merit and/or potential.

How to apply 

To be considered for a Scholarship for one of the above MSc programmes in academic year 2025-26, please apply for the course via the Imperial MSc graduate course admissions process by Friday 28th March 2025, and complete the scholarship application form by 11.00 am (UK time) on Friday 11th April 2025.

Other funding 

British Council STEM Scholarship for Women - now closed

The British Council STEM Scholarships for women 2023/24 applying for the MSc Environmental Data Science and Machine Learning programme are providing full scholarships for female students from Egypt.

TotalEnergies has a sponsorship opportunity for candidates who are applying for the MSc in Environmental Data Science & Machine Learning for autumn 2024 entry. See the details and apply for the sponsorship via Total Energies Careers page (view details in French).  Please note that you are required to make an application for study on the MSc EDSML course in addition to making an application for this sponsorship opportunity.

Aziz Foundation / Imperial College Environmental Data Science and Machine Learning Scholarship

The scholarship offers a unique opportunity for an exceptional Muslim student active in British Muslim communities and keen to build bridges with all parts of British society. The student will have the commitment and intelligence to support and represent Muslim communities in wider society, as well as the credibility to bring and explain new learning experiences back to the community, and will thus create mutual understanding between Muslims and others and contribute to the creation of a more cohesive society.

The scholarship will cover the full home tuition fee.

Applicants should meet the following requirements:

  • Hold an offer or have submitted a complete application for MSc Environmental Data Science and Machine Learning for October 2024 entry.
  • Be a UK national and eligible for Home fee status at the University.
  • Be active within their local and/or faith community and demonstrate a desire to develop intellectual skills within a multi-faith/secular environment.
  • Be able to demonstrate long-term commitment to community/societal development and good relations work through public leadership.
  • Be Zakat eligible/ in financial need and therefore cannot cover the costs of tuition.
  • Have an excellent understanding of their chosen industry and how to progress within it, with relevant, prior experience and/or achievements in the field.
  • Have knowledge of how this programme will enable them to generate social impact.
Please submit the statement to ese-msc-edsml@imperial.ac.uk as soon as possible. Remember to include your full name and your CID in your email. The deadline is 5th July 2024. 

Contacts

For further information please contact ese-msc-edsml@imperial.ac.uk.