MSc Data Science (Earth and Environmental Analytics) / Course details
Year of entry: 2026
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Course description
The inexorable rise of the digital world, driven by rapid AI development, has made data scientists more in demand right now than ever before. The advances of analysing big data span beyond the digital and technology industry and are increasingly recognised in the worlds of sport, medicine, space exploration and more. Our MSc Data Science (Earth and Environmental Analytics) course prepares you for a career in this high-demand field.
You’ll develop invaluable abilities in key area’s such as:
- data analysis;
- project design;
- computational methods;
- data stewardship.
See a full list of mandatory and optional course units below.
This course focuses on the data techniques and uses that are most relevant to environmental management, with optional course units exploring themes such as pollution control and subsurface geoscience.
We welcome applicants from a range of STEM, business and humanities backgrounds, allowing us to create a diverse cohort and enrich discussions around the uses and potential of data.
By the end of your studies, you will have developed a highly valued skillset, enhancing your employability across countless sectors such as policy, business, research and more. Previous students have gone on to roles such as data scientists, civil servants, consultants, researchers, entrepreneurs, and in AI.
Aims
This course will:
- Provide an opportunity for graduates from a broad range of disciplines to develop data science skills.
- Prepare you to understand and respond to the complex interactions between the environment, climate, natural ecosystems, human social and economic systems, and health.
- Train you to ask important research questions, evaluate the quality of available evidence, select appropriate methods and use analytical skills to visualise, interpret and provide strategic advice and insight.
- Enable you to develop into an agile, skilled data scientist adept at working in a variety of settings, able to meet the challenges and rewards of interdisciplinary teamwork.
- 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.
Special features
Interdisciplinary approach
Gain a comprehensive understanding of data analytics through studying varied aspects and applications of data science from Statistics, Demography, Social Networks, Data Science, Economics, Politics, Criminology, Health, Sociology, and other fields.
Hands-on
Make theory come alive with hands on experience analysing real-world data using a variety of statistical software such as R, Python, Excel and more.
Teaching and learning
This course is taught by an interdisciplinary team using a variety of delivery methods:
- lectures;
- computer based practicals;
- e-learning;
- meetings with industry partners;
- workshops;
- group work;
- individual research.
Coursework and assessment
Course units are assessed in a variety of ways, including:
- exams;
- essays;
- reports;
- online tests;
- video and in person presentations;
- presenting code files;
- group work;
- practical skills assessments.
Course unit details
A master’s degree is formed of 180 credits.
120 of these credits are made up by a mix of mandatory and optional course units, worth 15 credits each. You will need to select eight of these course units, with 60 credits taken each semester.
The core units are:
- Machine Learning and Statistics (both semesters);
- Understanding Databases;
- Understanding Data and their Environment;
- Applications in Data Science.
Optional course units range from statistical foundations to environmental based data analysis and GIS, preparing you with a varied but strong foundation in data and environmental analytics.
The availability of individual optional course units may be subject to change. Information that is sent to you in August about registration onto the course will clearly state the course units that are available in the academic year ahead.
The remaining 60 credits are awarded through a compulsory research component in the form of a 12,000-to-15,000-word dissertation. Your dissertation must be within the area of one of the course units you have chosen.
Your dissertation research is supported by weekly research methodology lectures designed to improve your academic, research and writing skills.
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.
| Title | Code | Credit rating | Mandatory/optional |
|---|---|---|---|
| Statistics and Machine Learning 1: Statistical Foundations | DATA70121 | 15 | Mandatory |
| Statistics & Machine Learning 2: AI, Complex Data, Computationally Intensive Statistics | DATA70132 | 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 |
| Privacy, Confidentiality and Disclosure Control | DATA70402 | 15 | Optional |
| Measuring and Predicting 2 | EART60071 | 15 | Optional |
| Computational Subsurface Geoscience | EART60152 | 15 | Optional |
| Key Interpretation Skills | EART60381 | 15 | Optional |
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