MSc Data Science (Earth and Environmental Analytics) / Course details

Year of entry: 2024

Course description

The range of pathways reflects the interdisciplinary nature of the course and we welcome applications from students with backgrounds in a range of disciplines, including:

  • Business and Management;
  • Health Science;
  • Social Sciences;
  • Geography;
  • Planning;
  • Computer Science;
  • Mathematics.

We provide training in core data science skills, embedded in a disciplinary context provided by the pathway, you will develop:

  • computational skills;
  • data analytical skills;
  • data stewardship skills and knowledge;
  • project design skills.


This innovative MSc in Data Science course is an opportunity for graduates from a broad range of disciplines to develop data science skills. Our goal is to help you develop into an agile, skilled data scientist, adept at working in a variety of settings and be able to meet the challenges and reap the rewards of interdisciplinary teamwork.  

There is growing evidence across academia, policy makers and industry that `Environmental Intelligence (EI)' is an emerging research area of strategic global importance. Its primary aim is to understand and respond to the complex interactions between the environment, climate, natural ecosystems, human social and economic systems, and health.    

Key areas of concern include:

  • Climate Change (e.g., resilient cities, clean power, smart transport options, sustainable production).
  • Biodiversity and Conservation (e.g., habitat protection and restoration, pollution control, realizing natural capital), Natural Hazards (e.g., predicting and forecasting, early warning systems, resilient infrastructure).
  • Environment and Health (e.g., air quality, water and sanitisation, social and environmental inequalities).   

Mapped to The University of Manchester's research strengths, the aim of this pathway is to prepare you for new and exciting training across cutting edge data science and environmental science technologies to integrate multiple complex data sources and create tools that enable informed decision making for future environmental systems.

Course unit details

Through a set of core units, you will develop a set of key data science skills.

The core units are:

  • Machine Learning and Statistics (both semesters);
  • Understanding Databases;
  • Understanding Data and their Environment;
  • Applying Data Science

Course unit list

The course unit details given below are subject to change, and are the latest example of the curriculum available on this course of study.

TitleCodeCredit ratingMandatory/optional
Statistics and Machine Learning 1: Statistical Foundations DATA70121 15 Mandatory
Understanding Databases DATA70141 15 Mandatory
Applying Data Science DATA70202 15 Mandatory
Understanding Data and their Environment DATA71011 15 Mandatory
Extended Research Project DATA72000 60 Mandatory
Measuring and Predicting 2 EART60071 15 Optional
Computational Subsurface Geoscience EART60152 15 Optional
Key Interpretation Skills EART60381 15 Optional
Earth and Environmental Data Science EART60702 15 Optional
Environmental Monitoring and Modelling EART62012 15 Optional
Pollution Management in Practice 2 EART63012 15 Optional
Digital Terrain Analysis GEOG60412 15 Optional
Environmental Remote Sensing GEOG60941 15 Optional
GIS and Environmental Applications GEOG60951 15 Optional
Fundamentals of Synthetic Aperture Radar (SAR) applied to Environmental Monitoring GEOG70632 15 Optional
Spatial Ecology GEOG71922 15 Optional
Climate, Environment and Development MGDI60552 15 Optional
Neighbourhood Planning Project PLAN60812 15 Optional
Displaying 10 of 18 course units

Disability support

Practical support and advice for current students and applicants is available from the Disability Advisory and Support Service. Email: