Applied Data Science (Environment and Sustainability)

The contemporary climate and environmental crises necessitate discipline-transcending solutions underpinned by an understanding of complex systems and models that draw from a data intensive world. Sustainable development is now a top priority in many industries and for many public and non-governmental organizations. The explosion of ‘Big Data’ in many sectors, including urban and rural planning, infrastructure, health and well-being, and sustainable energy systems, urgently demands individuals who can handle complex datasets, communicate effectively, and fully understand the techniques, pitfalls, and potential of advanced data science methods. In response, this programme immerses students in a fusion of state-of-the-art modelling, essential data science techniques, and exploration of economic, environmental, and societal issues surrounding sustainability practices and policies.

Drawing on contemporary research, and in collaboration with internationally renowned researchers and data curators, students are guided through relevant concepts and modern trends in modelling and data science and their application to the environmental sciences. This programme is aimed at enhancing the students’ understanding of and confidence in using real-world data and mathematical models to address the big environmental, social and economic issues of our time, by means of innovative research-led teaching and learning, practical examples and hands-on exercises.

In the first term, students develop core skills and understanding through the fundamentals module in data science, and explore foundational concepts and transdisciplinary methods for sustainability science. In the second term, students are exposed to state-of-the-art data science methods in the “Trends in Data Science and Artificial Intelligence” module, put their data science and modelling skills into practice by completing the interdisciplinary , inquiry-led module “Tackling Sustainability Challenges using Data and Models”, and can select from further Masters level modules in the life and environmental sciences. The third term comprises an advanced data science and modelling project.

The programme is interdisciplinary and outward facing in nature, and is hosted by the university’s Environment and Sustainability Institute, an interdisciplinary centre leading cutting-edge research into solutions to problems of environmental change. Throughout the programme, students are encouraged and supported to collaborate with industry, charities or public sector organizations as part of taught modules and their projects. Students are introduced to a wide variety of data analytic and modelling approaches and global open sourced datasets, and so develop sought-after, discipline-transcending skills, which they will be encouraged to put to direct application through use of scientific and high-performance computing platforms. The programme thus prepares students for an array of quantitative and analytical professions that are found at the core of both modern research and the digital economy. All learning happens in an environment that promotes confidence, equality, inclusivity and strong societal values for supporting the design of a sustainable world in line with the United Nations sustainable development goals.

The MSc in Applied Data Science (Environment and Sustainability) is a 1-year full-time programme of study at National Qualification Framework (NQF) level 7 (as confirmed against the FHEQ). The programme is divided into units of study called ‘modules’ which are assigned a number of ‘credits’. The credit rating of a module is proportional to the total workload, with 1 credit being nominally equivalent to 10 hours of work. The programme comprises 180 credits in total.

Interim Awards

If you do not complete the programme, you may be able to exit with a lower qualification.

Postgraduate Diploma: At least 120 credits of which 90 or more must be at NQF level 7.

Postgraduate Certificate: At least 60 credits of which 45 or more must be at NQF level 7.

The following tables describe the programme and constituent modules. Constituent modules may be updated, deleted or replaced as a consequence of the annual review of this programme. Details of the modules currently offered may be obtained from the College website: College to provide link

The table below outlines the structure of the MSc programme. In Term 1, you will take the core/compulsory module ‘Fundamentals of Data Science’, select one or two modules from the suite of ‘Fundamentals in Environment and Sustainability’ options and/or one option from the below list of recommended modules in the life, environmental or health sciences. In Term 2, you will take two further core/compulsory modules and either a complimentary ‘Fundamentals in Environment and Sustainability’ option or one option from the below list of recommended modules in the life, environmental or health sciences. Every module has its own assessment criteria, details of which are provided in the module descriptors. You may take optional modules provided that any necessary prerequisites have been satisfied; you have the module convenor’s agreement; there are no timetable conflicts; and you have not already taken the module in question or an equivalent module.

In Term 3, you will conduct an individual research project/dissertation that encapsulates the skills and knowledge you have developed during the taught sections of the programme. You may propose your own research topic, typically in collaboration with an academic supervisor, or select from a list of projects provided. In this research project you will apply data science and AI skills to a challenge in environmental and/or sustainability science. Supervision will be provided by experts in both data science and environmental science, and may involve industry, public or third sector organisations. The project should be predominantly of a research nature and aim to make a small but unique contribution to your chosen subject area, with an appropriate balance of data science and application. It will lead to a dissertation submission and presentation (outlined in the module descriptor), with the dissertation submitted at the end of the academic year.

Stage 1: 135 credits of compulsory modules, 45 credits of optional modules

Full details of assessment regulations for all taught programmes can be found in the TQA Manual, specifically in the Credit and Qualifications Framework, and the Assessment, Progression and Awarding: Taught Programmes Handbook.

Additional information, including Generic Marking Criteria, can be found in the Learning and Teaching Support Handbook.

Each academic programme in the University is subject to an agreed College assessment and marking strategy, underpinned by institution-wide assessment procedures.

The security of assessment and academic standards is further supported through the appointment of External Examiners for each programme. External Examiners have access to draft papers, course work and examination scripts. They are required to attend the Board of Examiners and to provide an annual report. Annual External Examiner reports are monitored at both College and University level. Their responsibilities are described in the University's code of practice. See the University's TQA Manual for details.

Certain programmes are subject to accreditation and/or review by professional and statutory regulatory bodies (PSRBs). This programme is not subject to any such requirements.