New Environmental Data Science and Machine Learning MSc, starting October 2021

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.

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.

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.

How to apply

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 interested in Environmental Data Science and Machine Learning MSc, we strongly encourage you to contact Joanna Owens, 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.

Contacts

For further information please contact the Course Director or Postgraduate Education Administrator.

Course Director: Professor Matthew PiggottEmail Matthew 

Postgraduate Education Administrator: Joanna Owens, Email Joanna
+44 (0) 207 594 6462
Department of Earth Science and Engineering
Imperial College London

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

Mathematical Modelling

Environmental Data

Term 2

Applying Computational/Data Science (several short group projects)

Advanced programming

Big Data Analytics

Inversion and Optimisation

Machine Learning

Term 3 (summer)

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.

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 and further 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 interested in Environmental Data Science and Machine Learning MSc, we strongly encourage you to contact Joanna Owens, prior to starting your application.