Environmental Data Analytics MSc

The core principle here is: "You can't give what you don't have”. Before you can deploy AI, machine learning, GIS or remote sensing to solve environmental problems, you need to understand the components of the environment.

Therefore, this module explores the five key spheres of the environment: the atmosphere, hydrosphere, lithosphere, biosphere and anthroposphere. Learn how they interact to shape our planet’s climate system. Get a scientific understanding of climate change, its impacts on both natural and human systems, and the importance of planning for mitigation and adaptation.

Explore emerging approaches such as carbon trading, which provides insights into the science and policy dimensions of climate action.

Compulsory

Learn how to combine Geographic Information Systems (GIS), remote sensing and environmental data science to understand complex environmental challenges. Explore how these tools work together to improve predictive environmental modelling and support more accurate, evidence-based decision-making.

Using real-world examples such as air quality monitoring, natural disaster management (including earthquakes and wildfires), and soil and water conservation, apply advanced data processing techniques and explore how machine learning algorithms can enhance GIS and remote sensing applications. By the end of this module, you should have the skills to use these technologies in practical, impactful ways to address global environmental issues.

Compulsory

Explore programming and machine learning, focusing on practical applications to environmental data. Using a discovery-based learning approach, gain a foundational understanding of machine logic, become familiar with programming syntax for basic environmental data analysis, and develop your coding skills. Learn core principles of algorithmic thinking and coding techniques. While the aim is not to reach the level of a computer programmer/specialist, you will be supported in building confidence and further developing your skills through self-paced practice.

Machine learning (deep learning and neural networks) encompasses a wide range of intelligent methods and algorithms, including data preparation and pre-processing, supervised and unsupervised learning, reinforcement learning, classification, clustering, and future directions in the field. This module also draws on principles from computer science, statistics, mathematics, and complexity science. By engaging with these areas, you should gain a deeper appreciation of the interconnected nature of mathematics, programming, and statistics - essential foundations for environmental data science and machine learning.

Compulsory

Databases sit at the heart of how we store, manage and use big data. Master database management systems (DBMS) to organise and control large-scale data effectively. You will learn how DBMS supports key tasks like data integration, security and running queries that turn raw information into meaningful insights.

The course also covers the "five Vs" (volume, velocity, veracity, variety and value), alongside the core components of modern data systems. Apart from developing technical skills, you should learn to think critically about the big picture. Examine the social, ethical, and moral issues around AI and data usage, including how AI systems affect people and society, and the importance of designing them safely and responsibly.

Compulsory

If you secure a placement² in industry within your first semester, you may have the option to extend your 12-month master’s course by up to one additional year to complete the placement before starting your final 60-credit module.

• Gain valuable industry insights and skills as you apply your academic knowledge and skills in a professional placement.
• You will be responsible for sourcing and securing your placement during your first semester. Our careers and employability team can provide guidance and support throughout the application process.
• Start your professional placement after completing the first 120 credits of your course.
• This professional placement will be integrated into your course learning and outcomes. This will take the form of a reflective diary demonstrating your learning and engagement with the placement experience.
• The professional placement option is subject to additional fees, living costs, visa requirements, availability, competitive application and university approval of the placement.² For full terms and conditions, please visit the enhanced master's landing page.

Optional

Take on an independent research project in your chosen area, such as sustainability, environment (for example, water, air, soil and biodiversity), energy, or related disciplines, and work under the guidance of an experienced academic supervisor.

Learn how to identify and define a research problem, review existing literature and design a suitable methodology. Along the way, you should strengthen your skills in project management, data collection, analysis and interpretation. Consider important aspects like risk assessment and research ethics. By the end of this module, you should have planned, managed and delivered a substantial piece of independent research, building valuable expertise for both academic and professional contexts.

Compulsory