Master of Data Science (Environmental Data Science)
MDS
1 year full-time
Durham City
G5P723
Course details
Learn to harness the power of environmental data to address global challenges. This conversion course equips you with the skills to analyse complex spatial and temporal datasets, using cutting-edge tools to inform sustainable solutions in science, industry, and policy.
From personalised medicine, to smart cities and sustainable solutions, data science is building a better world. At the same time, developments in technology have made the field of data science more accessible than ever, creating new opportunities to gain insight into the interactions between people and their environment. This has led to a significant increase in demand for skilled data scientists, and this demand is predicted to further grow.
Drawing on this, we have created the Master of Data Science (Earth and Environment), a conversion course that equips you with the skills to access, clean, analyse and visualise data, opening a future in data science even if your first degree is in a non-quantitative subject. It is likely to appeal to geographers, earth and environmental scientists who want to learn how to use the data produced in modern industry, science and government in the management of natural resources and spatio-temporal information flows.
The course provides training in contemporary data science. You will be based in a supportive environment, learning from practicing researchers who are making a difference across a range of industries. Shared core modules across the suite of MDS courses will equip you with wider statistical and machine learning skills, while subject-specific earth and environment modules develop your quantitative skills in the field of natural resources. It is equally suitable whether you are planning to use quantitative analysis in a research capacity, or if you are a geography or environmental graduate who wants to learn transferable data and modelling analysis skills.
The MDS culminates in the research project, an in-depth investigation in which you apply the skills learned during the course to a research problem working alongside an expert in the area of application of your choice. There may be an option to carry out the project in conjunction with an industry partner.
The course begins with a range of introductory modules before progressing to more advanced contemporary techniques such as neural networks, analysis of spatial and temporal datasets and deep learning. Optional modules allow you to focus on an area of interest.
Course structure
Year 1 modules
Core modules:
The Data Science Research Project
is a substantial piece of self directed research into an unfamiliar area of data science, or in your subject specialisation area with a focus on data science. The project can be practical, theoretical or both, and is designed to develop your research, analysis and report-writing skills.
Critical Perspectives in Data Science and AI
develops your understanding of the production, analysis and use of quantified data, and how to analyse these practices anthropologically utilising AI applications. You will learn to think ethically and contextually about quantified data, and how to apply this knowledge to practical problems in data science, including your own research project.
Data Science Applications in Earth Sciences
provides experience of handling, amalgamating and analysing diverse earth and environmental datasets from a range of sources and across a range of spatial and temporal scales. You will also use datasets to address problems at the forefront of earth and environmental sciences, across a range of topics and explore and use popular software packages currently used in industry settings.
Data Analysis in Space and Time
provides an understanding of data methods and tools used in the field of earth and environmental sciences, with a particular focus on those used for analysing spatial and temporal datasets. You will also learn about the physical modelling of complex real-world systems and use popular software packages currently used in industry settings.
Programming for Data Science
uses the popular Python software packages used in a wide range of industry settings. You will learn how to gather, manipulate and process real-world data and learn the key concepts of data analysis and data visualisation.
Introduction to Statistics for Data Science
focuses on the fundamentals of statistics you will need for data science. The module covers topics such as exploratory statistics, statistical inference; linear models; classification and clustering methods; and resampling and validation.
Machine Learning
introduces the essential knowledge and skills required in machine learning for data science using the R statistical language. You will develop an understanding of the theory, computation and application of topics such as modern regression methods, decision-based machine-learning techniques, support vector machines, and neural networks.
Optional modules:
The remainder of the course will be made up of core and option modules which will vary depending on prior qualifications and experience. These have previously included:
- Introduction to Computer Science
- Introduction to Mathematics for Data Science
- Text Mining and Language Analytics
- Data Exploration, Visualisation, and Unsupervised Learning
- Strategic Leadership
- Ethics of Artificial Intelligence and Data Science
Learning
This interdisciplinary course is made up of modules that span departments across the University. It incorporates a wide range of learning and teaching methods which vary according to the modules studied. These include lectures, seminars, workshops and computer/practical classes. The taught elements are further reinforced through independent study, group work, research and analysis, case studies and structured reading.
All modules are underpinned by research and embed elements of research training in both delivery and assessment. Throughout the course you will be encouraged to develop research methods, skills and ethics reflecting the methods used by the research-active staff. Overall, you will be encouraged and guided to be ‘research minded’ in all modules, and to develop these critical skills for use in future work or research.
Assessment
The Master of Data Science (Environmental Data Science) is assessed via a combination of essays, online assessments, reports and presentations – both individual and in small groups.
The course culminates in a major research project, which is conducted and written up as an independent piece of work with support from your appointed supervisor.
Entry requirements
We require a 2:1 Bachelor (Honours) degree or international equivalent in any degree subject.
Applicants with a strong data science or mathematical background may wish to consider our MSc Scientific Computing and Data Analysis programmes.
Alternative qualifications
International students who do not meet direct entry requirements for this degree might have the option to complete an International Foundation Year.
Home students who do not meet our direct entry requirements, may be eligible for our Foundation Programme which offers multidisciplinary programmes to prepare you for a range of specified degree programmes.
Fees and funding
The fees for this academic year have not been confirmed yet.
Please also check costs for colleges and accommodation.
Scholarships and Bursaries
We are committed to supporting the best students irrespective of financial circumstances and are delighted to offer a range of funding opportunities.
Find out more about Scholarships and BursariesCareer opportunities
Natural Sciences
In today’s data-driven world, the ability to capture, analyse, and communicate insights from complex information is one of the most sought-after skills by employers globally.
Graduates are equipped for exciting and meaningful careers across a wide range of sectors from science, health, and the environment to the humanities and social policy where data is driving innovation and transforming how we live and work. Whether you're influencing public health strategies, advancing climate research, or unlocking cultural insights, your data science expertise will open doors to impactful opportunities.
Department information
Natural Sciences
Our Master of Data Science programmes give you the tools to capture, analyse, and unlock insights from the vast streams of data shaping our world. These interdisciplinary degrees empower smarter decision-making across sectors from health and environment to culture and society.
The Master of Data Science programmes at Durham University equip you with the skills to capture, process, and analyse the vast and complex data shaping every aspect of modern life. From healthcare and environmental science to social policy and cultural heritage, data science is transforming how decisions are made and problems are solved.
The suite of programmes are designed as conversion degrees, ideal for students from non-data science backgrounds who want to transition into this high-impact field. You'll gain a strong foundation in data science while exploring its applications across a range of disciplines.
Choose from seven pathways tailored to your interests and career goals: the Master of Data Science or Master of Data Science with AI Applications, or specialist routes in Bioinformatics and Biological Modelling, Digital Humanities, Earth and Environment, Health, Social Analytics, and Heritage.
As a student, you’ll benefit from Durham’s interdisciplinary approach and the expertise of the Institute for Data Science, a hub for innovation that supports research and teaching across the University. Join a vibrant academic community working to transform nature, society, and culture through data.
For more information see our department pages.
Facilities
Data Science is a conversion course that incorporates content from many Departments across the University.
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