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Study information

Applied Data Science and Modelling (2023)

1. Programme Title:

Applied Data Science and Modelling

NQF Level:


2. Description of the Programme (as in the Business Approval Form)

The concurrent crises of health, climate and environment require interdisciplinary solutions underpinned by sophisticated contemporary understanding and modelling in a data-intensive world. This programme immerses students in a fusion of the “Big Data revolution” and modern mathematical modelling within a challenges-led learning environment. Drawing from the latest research, and in collaboration with internationally renowned investigators and data curators at the University’s Environment and Sustainability Institute, European Centre for Environment and Human Health, and Centre for Ecology and Conservation, students will be guided through relevant concepts and modern trends in mathematical modelling, data science and artificial intelligence (AI).

3. Educational Aims of the Programme

This programme is aimed at enhancing the students’ understanding of and confidence in using real-world data and mathematical models to address the big societal issues of the 21st century, by means of research-led teaching, practical examples and hands-on exercises. In the first term, students develop the foundation and core computational skills in data science and modelling through the “Fundamentals in Data Science” and “Computational Modelling and Simulation” modules. In the second term, students are exposed to state-of-the-art methods in the “Trends in Data Science and AI” module, can select from options including “Complex Systems” and “Applied AI and Control”, and put their data science and modelling skills into practice by completing the interdisciplinary, inquiry-led module “Tackling Sustainability Challenges using Data and Models”. The third term comprises an advanced data science and modelling project.

The programme is interdisciplinary and outward facing in nature, and 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 analytics and modelling approaches, providing them with sought-after, discipline-transcending skills, which can be put to direct application through use of scientific and high-performance computing platforms. 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.

4. Programme Structure

The MSc in Applied Data Science and Modelling 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.

5. Programme Modules

The following table describes 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.

The table below outlines the structure of the MSc programme. In Term 1, you will take two core/compulsory modules. In Term 2, you will take two core/compulsory modules and select 1 of the 2 optional modules. Every module has its own assessment criteria, details of which are provided in the module descriptors. 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. This research project may take any of several forms — it may be data science or modelling-based, theoretical or application focused, or any combination thereof. The project should be predominantly of a research nature and aim to make a small but unique contribution to your chosen subject area. 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

Code Title Credits Compulsory NonCondonable
MTHM601Fundamentals of Data Science30YesNo
MTHM602Trends in Data Science and AI15YesNo
MTHM603Data Science and Modelling Dissertation60YesNo
MTHM604Tackling Sustainability Challenges using Data and Models 30YesNo
MTHM607Computational Modelling and Simulation30YesNo
Select 15 credits from:
MTHM605Complex Systems15NoNo
MTHM606 Applied AI and Control15NoNo

6. Programme Outcomes Linked to Teaching, Learning & Assessment Methods

On successfully completing the programme you will be able to: Intended Learning Outcomes (ILOs) will be accommodated & facilitated by the following learning & teaching and evidenced by the following assessment methods:

A Specialised Subject Skills & Knowledge

1.Select appropriate data science and AI methods to detect, model and understand patterns in data.

2. Apply a range of modelling techniques in the appropriate context.

3. Conceive and realise appropriate data analysis designs and models to address real-world problems.

4. Communicate the results of complex data analyses, including an understanding of how subsequent analyses are affected by the source of data and how it was collected.

5. Communicate the results of computational models and complex computer simulations, including their significance and shortcomings.

6. Understand the societal context of data science and modelling, including key aspects of data governance, legal requirements, and ethical considerations.

Learning & Teaching Activities

Lectures, workshops, seminars, practicals, online materials and formal training. Each module also has core and supplementary texts, or material recommended by module deliverers, which provide in-depth coverage of the subject beyond lecture content.

Students are given clear guidance in how to manage their learning. Project work, involving real-world data and case studies, is used extensively to integrate material and make knowledge and skills functional.

Assessment Methods

The assessment approach for each module is explicitly stated in the full module description given to students.

Assessment methods will include:

Quizzes (ILO1 and 3), written reports (ILO1-6), practical exercises in coding and data analysis (ILO1 and 3), practical exercises in model design and analysis (ILO2 and 3), presentations (ILO4-6)

B Academic Discipline Core Skills & Knowledge

7. Understand the methodology, and practical use, of data science, AI and mathematical modelling.
8. Select and apply appropriate methods based on the problem being addressed.
9. Perform critical appraisal of relevant academic and technical literature, and algorithms.
10. Understand the technical details behind new methods and appraise their suitability before applying them.
11. Handle large and complex datasets effectively and prepare them for analyses.
12. Understand the importance of data visualisation within data science, AI and modelling.
13. Develop appreciation of suitable approaches in communication of results to different audiences.
14. Understand the consequences of legal and regulatory requirements for data privacy, ethical use of data, and data governance.
15. Exhibit professional level ICT skills in course work, research and presentation.

Learning & Teaching Activities

Lectures, workshops, seminars, practicals, online materials and formal training. Each module also has core and supplementary texts, or material recommended by module deliverers, which provide in-depth coverage of the subject and go beyond the lectures.

Assessment Methods

The assessment strategy for each module is explicitly stated in the full module description given to students. Group and team skills are addressed within modules dealing with specialist and advanced skills.

Assessment methods will include:

Quizzes (ILO7, 9 and 10), written reports (ILO7-15), practical exercises in coding and data analysis (ILO7, 8, 10-12 and 15), practical exercises in model design and analysis (ILO7, 8, 11-12 and 15), presentations (ILO7, 9, 10, 12-15).

C Personal / Transferable / Employment Skills & Knowledge

16. Effectively communicate methods and results based on analysis of complex problems in both written reports and oral presentations.

17. Demonstrate awareness of tools and technologies relevant to data science, AI and modelling.

18. Design and manage a data analysis and/or modelling project from initiation to final report.

19. Work effectively, both independently and in teams.

20. Demonstrate leadership in managing team work.

Learning & Teaching Activities

Lectures, workshops, seminars, practicals, online materials and formal training. Each module also has core and supplementary texts, or material recommended by module deliverers, which provide in-depth coverage of the subject and go beyond the lectures.


Assessment Methods

The assessment strategy for each module is explicitly stated in the full module description given to students.

Assessment methods will include:

Written reports (ILO16-19), executive report by group lead (ILO19-20), practical exercises in coding and data analysis (ILO17-18), practical exercises in model design and analysis (ILO17-18), presentations (ILO16-19).

7. Programme Regulations

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.

8. College Support for Students and Students' Learning

In accordance with University policy a system of personal tutors is in place for all students on this programme.  A University-wide statement on such provision is included in the University's TQA Manual.  As a student enrolled on this programme you will receive the personal and academic support of the Programme Coordinator and will have regular scheduled meetings with your Personal Tutor; you may request additional meetings as and when required. The role of personal tutors is to provide you with advice and support for the duration of the programme and extends to providing you with details of how to obtain support and guidance on personal difficulties such as accommodation, financial difficulties and sickness. You can also make an appointment to see individual teaching staff.

Online Module study resources provide materials for modules that you are registered for, in addition to some useful subject and IT resources. Generic study support resources, library and research skills, past exam papers, and the 'Academic Honesty and Plagiarism' module are also available through the student portal (

Student/Staff Liaison Committee enables students & staff to jointly participate in the management and review of the teaching and learning provision.

Your lead Department will be Mathematics. You will be taught and supervised by staff from Mathematics, with guest lectures and, where applicable, project co-supervision by staff from Geography, the Centre for Ecology and Conservation, Renewable Energy, the Environment and Sustainability Institute (all based on Penryn campus), and/or the European Centre for Environment and Human Health (based on Truro campus). You can expect reasonable access to all teaching staff through appointments and will in addition receive formative feedback from various discussion groups/in-lecture exercises throughout the delivery of each module and therefore receive essentially continuous feedback during the taught component of the programme. Project supervisors provide academic and tutorial support once students move on to the research (Dissertation) component of the course. Student progress will be monitored and you can receive up-to-date records of the assessment, achievements and progress at any stage.

10. Admission Criteria

All applications are considered individually on merit. The University is committed to an equal opportunities policy with respect to gender, age, race, sexual orientation and disability when dealing with applications. It is also committed to widening access to higher education to students from a diverse range of backgrounds and experience.

Candidates must satisfy the general admissions requirements of the University of Exeter.

Entry requirements

Candidates will be required to have a good degree (at least a 2:2), or equivalent qualification.

Successful applicants will usually have at least an A-level or equivalent in mathematics and/or have received quantitative skills training as part of their undergraduate programme or professional experience.

Prior experience of coding is not necessary on this programme.

For those whose native language is not English, evidence of competence in the English language will be required and, after admission to the University, they may be given the opportunity to take additional language instruction, normally at the University INTO Language Centre. IELTS (International English Language Testing System) and TOEFL (Test of English as a Foreign Language) are acceptable for evidence; details of these can be found in the Graduate School Prospectus. For an unconditional offer, scores of IELTS - 7-9 (with 6.0 in writing), TOEFL - 250-300 (4.0 in essay writing), (paper based TOEFL score 590-677) are required. However, if the student has successfully undertaken a full degree programme in an English speaking country, e.g. UK, USA, Australia, this requirement will normally be waived provided that the degree was taken no more than five years before the start of proposed study here. Other qualifications may also be considered.

We actively promote the University’s policies with regard to equality of opportunity. Admissions information relating to disability can be found at

11. Regulation of Assessment and Academic Standards

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.

12. Indicators of Quality and Standards

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.

14 Awarding Institution University of Exeter
15 Lead College / Teaching Institution College of Engineering, Mathematics and Physical Sciences
16 Partner College / Institution
17 Programme accredited/validated by External bodies (PSRB) that have endorsed this programme
18 Final Award(s) MSc
19 UCAS Code (UG programmes) APDATSCIMOD
20 NQF Level of Final Awards(s): 7
21 Credit (CATS and ECTS) 180
22 QAA Subject Benchmarking Group (UG and PGT programmes) Mathematics, Statistics and Operational Research (MMath) 2015
23 Origin Date February 8th 2023 Last Date of Revision: February 8th 2023