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Study information

Mathematics and Data Science (2023)

1. Programme Title:

Mathematics and Data Science

NQF Level:


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

The MSci Mathematics and Data Science is an innovative inter-disciplinary taught programme designed with industry and aimed at students wishing to work or research in data science. The programme will cover the core areas of mathematics (mathematical methods; probability, statistics and data; statistical modelling and inference) and computer science (programming and object oriented programming). It will also include new modules which will introduce students to applied data science (e.g. machine learning, data structure and algorithm, Artificial Intelligence (AI) and applications, computational intelligence, HPC, Big Data, Cloud) as well as social context (e.g. governance, ethics, business applications).  Taught modules in the 1st and 2nd years provide the mathematical background to 3rd year modules which will include options in weather and climate theory and mathematical biology.  Research projects in each academic year will allow students to develop research and project management skills in an area of interest, using real world datasets, guided by a leading academic supervisor.


3. Educational Aims of the Programme

The programme is intended to:

a) Provide a high quality general education mathematics and data science comprising a balanced core of key knowledge together with the opportunity to study a range of selected topics in more depth, some at an advanced level;

b) Provide the foundations needed for those intending to become professional or research mathematicians or data scientists;

c) Develop the analytical abilities of students so that they can identify and apply appropriate mathematical and data science techniques and methods to solve problems in a range of application areas;

d) Develop in students appropriate subject-specific, core academic and personal and key skills in order to prepare them for a wide range of employment opportunities, including a research career;

e) Generate in students an enthusiasm for the subject of mathematics and data science and involve them in a demanding, interesting and intellectually stimulating learning experience reinforced by appropriate academic and pastoral tutorial support.


4. Programme Structure

Your Mathematics and Data Science programme is a (4) year programme of study at National Qualification Framework (NQF) level (7) (as confirmed against the FHEQ). This programme is divided into (4) ‘Stages’. Each Stage is normally equivalent to an academic year.  The programme is also 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.

Interim Awards

If you do not complete the programme you may be able to exit with a lower qualification. If you have achieved 120 credits, you may be awarded a Certificate of Higher Education in Mathematics with Data Science and if you achieve 240 credits, where at least 90 credits are at level 2 or above, you may be awarded a Diploma of Higher Education in Mathematics with Data Science. If you have achieved 360 credits, then you may satisfy the requirements for the award of BSc (Hons) Mathematics and Data Science (please refer to the BSc (Hons) Mathematics and Data Science programme specification for detail).


5. Programme Modules

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 Faculty web site:

You may take Option Modules as long as any necessary prerequisites have been satisfied, where the timetable allows, and if you have not already taken the module in question or an equivalent module. Descriptions of the individual modules are given in full on the Faculty web site:

You may take Elective Modules outside of the programme of up to 15 credits in the second stage and 30 credits in the third and fourth stages of the programme as long as any necessary prerequisites have been satisfied, where the timetable allows and if you have not already taken the module in question or an equivalent module.  Elective modules may not be at a stage more than one stage behind your current stage.


Stage 1

Code Title Credits Compulsory NonCondonable
ECM1410Object-Oriented Programming15YesYes
MTH1002Mathematical Methods30YesNo
MTH1004Probability, Statistics and Data30YesNo
COM1011Fundamentals of Machine Learning15YesNo
COM1012Data Science Group Project 115YesNo

Stage 2

Code Title Credits Compulsory NonCondonable
MTH2003Differential Equations15YesNo
MTH2004Vector Calculus and Applications15YesNo
MTH2006Statistical Modelling and Inference30YesNo
COM2011Machine Learning and Data Science15YesNo
COM2012Data Science in Society15YesNo
COM2013Data Science Group Project 215YesNo
Select 30 credits from:
COM2014Computational Intelligence15NoNo
*Free choice elective15NoNo

The free choice (electives) can include modules from any College in the University subject to approval, pre-requisites, timetabling and availability.

Standard progression to Stage 3 of the MSci: Candidates will have passed all 120 credits of Stage 2 modules each with an overall mark of 40% or higher, and will normally have gained a stage average of 55% or higher. Students who do not reach the threshold may progress to stage 3 of the equivalent BSc programme.



Stage 3

Code Title Credits Compulsory NonCondonable
COM3021Data Science at Scale15YesNo
COM3022Data Science Individual Project 130YesNo
COM3023Machine Learning and AI15YesNo
Select three modules (45 credits) at level 6, to include at least 2 of the following:
MTH3001Theory of Weather and Climate15NoNo
MTH3006Mathematical Biology and Ecology15NoNo
MTH3007Fluid Dynamics15NoNo
MTH3008Partial Differential Equations15NoNo
MTH3019Mathematics: History and Culture15NoNo
MTH3022Graphs, Networks and Algorithms15NoNo
MTH3024Stochastic Processes15NoNo
MTH3028Statistical Inference: Theory and Practice15NoNo
MTH3030Mathematics of Climate Change15NoNo
MTH3041Bayesian statistics, Philosophy and Practice15NoNo
MTH3042Integral Equations15NoNo
MTH3044Bayesian Data Modelling15NoNo
MTH3045Statistical Computing15NoNo
MTH****Any other level 2 or 3 Mathematics module15NoNo



Stage 4

Code Title Credits Compulsory NonCondonable
ECMM427Group Development Project 30YesNo
COMM032Data Science MSci Individual Project30YesNo
Select 60 credits:
MTHM009Advanced Topics in Mathematical & Computational Biology15NoNo
MTHM017Advanced Topics in Statistics15NoNo
MTHM018Dynamical Systems and Chaos15NoNo
MTHM019Fluid Dynamics of Atmospheres and Oceans15NoNo
MTHM023Modelling the Weather and Climate15NoNo
MTHM030Waves, Instabilities and Turbulence15NoNo
MTHM031Magnetic Fields and Fluid Flows15NoNo
MTHM033Statistical Modelling in Space and Time15NoNo
MTHM042Advanced Probability Theory15NoNo
MTHM045Space Weather and Plasmas15NoNo
MTHM048Ergodic Theory15NoNo
MTHM052Mid-Latitude Weather Systems15NoNo
Any other level 4 Mathematics module(s)15NoNo
ECMM410Research Methodology15NoNo

Students may choose up to 30 credits of NQF Level 7 modules which are not listed above, either from within or outside the Faculty, subject to approval, timetabling and satisfaction of prerequisites.

Not all modules will be available every year, and new modules may be made available from time to time.


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

Demonstrate an understanding of:

1) The terminology and conventions used in mathematics;

2) A range of fundamental concepts and techniques from calculus, vectors, dynamics, probability, statistics, machine learning, programming, data science and AI;

3) The breadth of topics that can be tackled by mathematics and data science the use of the key techniques in a range of applicable areas;

4) A selection of specialist optional topics in mathematics, statistics and data science;

5) A deeper insight into selected more advanced areas of mathematics and data science and their applications;

6) How to use data to answer real world problems in longer projects and how to present results to non-specialists;

7) The ethics involved in using data and data science;

8) Work on a substantial independent project relating to an advanced topic at the interface of research mathematics and data science;

9) Research methods and techniques in mathematics and data science.


Learning & Teaching Activities

Knowledge in (1-4) is primarily provided through formal lectures supported by regular problem sheets for students to tackle on their own.

At Stages 1 and 2 lectures are reinforced by regular tutorial groups, either in class or in computer labs, in which assistance with, and feedback on, problem sheets is given.

At later stages in the programme students work on set problems by themselves, or in groups and seek help when required using the office hours of staff.

Applications of mathematics and data science (3) are introduced in various Stage 1 and 2 modules and more advanced applications are introduced in Stage 3 options.

Modules at Stage 3 encompass a range of special topics in mathematics, statistics and data science (4), and at stage 4 a number of advanced topics are offered (5).

Knowledge in (6) is provided through projects undertaken at stages 1-3 where students are given data and asked to produce posters, oral presentations and/or reports to communicate insight to non-specialists, either through the direction provided by a specific problem, or through open exploration.

(7) is provided through a module at Stage 2, but also through the projects and through modules in statistics and data science where various ethical issues are discussed as new concepts are introduced.

Knowledge in (8) and (9) is acquired in the final stage individual project.


Assessment Methods

Most knowledge is tested through formal examinations and coursework. Assessment of some modules involves essays, project reports, oral presentation or computer practicals.


B Academic Discipline Core Skills & Knowledge

1) Think logically;

2) Understand and construct mathematical proofs;

3) Formulate, analyse and solve problems;

4) Organise tasks into a structured form;

5) Transfer appropriate knowledge and methods from one topic within the subject to another;

6) Apply a range of ideas from mathematics, statistics and data science to unfamiliar problems and demonstrate good selection of choice in solution strategy;

7) Demonstrate a capacity for critical evaluation of arguments and evidence;

8) Present mathematical material clearly, logically and accurately, both in writing and orally;

9) Be able to work with data in a wide variety of forms, including the ability to convert data into forms suitable for applying techniques in statistics and machine learning;

10) Choose appropriate techniques for analysing data to gain insight into real world problems;

11) Report the results of data analysis ethically and clearly in a manner appropriate to a variety of types of target audience;

12) Plan, execute and report on a substantial project and defend the results.


Learning & Teaching Activities

All these skills are an essential part of the understanding of mathematics and data science, are embedded throughout core elements of the programmes and are intrinsic to good performance in the programmes.

They are developed through formal lectures, tutorials, coursework, computer practicals, use of IT and private study.

Skills (6-8) in particular are reinforced in optional modules involving directed reading, seminars or project work at Stage 3.


Assessment Methods

All these skills are tested indirectly in various core elements of the programmes, with (5-8) contributing particularly to the more successful work.

They are all assessed in part through written coursework and in part by unseen formal examinations.

Skills (6-11) are directly assessed in some modules via oral presentation, essays or project reports. The individual project assesses (12).


C Personal / Transferable / Employment Skills & Knowledge

1) Use a range of IT software including standard and mathematical word-processing applications, mathematical software and programming languages including R and Python;

2) Communicate ideas effectively and clearly by appropriate means including oral presentation;

3) Manage time effectively;

4) Search and retrieve information from a variety of sources including libraries, databases and the web;

5) Work as part of a team;

6) Plan their career and personal development;

7) Demonstrate independent learning ability required for continuing professional development.


Learning & Teaching Activities

Skill (1) is developed from Stage 1 through use of R and Python in core Stage 1 modules.

Skills (1-2) are developed in various other core components of the programme e.g. oral presentations in Stage 1 tutorials, and the requirement for submission of word-processed coursework in some assignments in certain modules at Stages 1 and 2.

Skill (3) is intrinsic to successful completion of the programme.

Skills (4) and (5) are developed in one of the core modules at Stage 1 and through the requirement to complete projects at all stages.

Skill (6) is reinforced through annual self-appraisals with personal tutors.


Assessment Methods

Skills (1-3) are indirectly assessed as part of coursework in core modules, and effective use of skills (1-4) will generall enhance performance throughout the programme.

Skills (1-5) are more directly assessed in one of the core modules are Stage 1 and at Stage 3 through the requirement to complete either a project or a specified alternative module at Stage 3.

Skill (7) is assesed through the final year individual project.


7. Programme Regulations


The programme consists of 480 credits with 120 credits taken at each stage. Normally not more than 75 credits would be allowed in any one term. In total, participants normally take no more than 150 credits at level 4, and must take at least 210 credits at level 6 or higher of which at least 120 must be at level 7.

The pass mark for award of credit in an individual module is 40% for modules taken at NQF Levels 4, 5 and 6 and 50% for modules taken at Level 7.


Condonement is the process that allows you to be awarded credit (and so progress to the next stage or, in the final stage, receive an award), despite failing to achieve a pass mark at a first attempt. You are not entitled to reassessment in condoned credit.

Up to 30 credits of failure can be condoned in a stage on the following conditions:

  1. You must have registered for and participated in modules amounting to at least 120 credits in the stage.
  2. You must pass the modules marked with a 'Yes' in the 'non-condonable' column in the tables above.
  3. In stages 1-3 you must achieve an average mark of at least 40% across the full 120 credits of assessment, including any failed and condoned modules. In the final stage you must achieve an average mark of at least 50% across the full 120 credits of assessment, including any failed and condoned modules.

Assessment and Awards

Assessment at stage one does not contribute to the summative classification of the award. The award will normally be based on the degree mark formed from the credit-weighted average marks for stages 2 and 3 and 4 combined in the ratio 2:3:4 respectively.


The marking of modules and the classification of awards broadly corresponds to the following percentage marks:

Class I    70% +                                                      

Class II   Division I 60-69%                                     

Class II   Division II 50-59%                                    

Class III  40-49%

Full details of assessment regulations for UG programmes can be found in the Teaching Quality Assurance Manual (TQA) on the University of Exeter website.  Generic marking criteria are also published here.

Please see the Teaching and Quality Assurance Manual for further guidance.


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.

Information Technology (IT) Services provide a wide range of services throughout the Exeter campuses including open access computer rooms, some of which are available 24 hours, 7 days a week.  Help may be obtained through the Helpdesk, and most study bedrooms in halls and flats are linked to the University's campus network.

Additionally, the Faculty has its own dedicated IT support staff, helpdesk and computer facilities which are linked to the wider network, but which also provide access to some specialised software packages.  Email is an important channel of communication between staff and students in the Faculty and an extensive range of web-based information (see ) is maintained for the use of students, including a comprehensive and annually revised student handbook.

The Harrison Learning Resource Centre is generally open during building open hours. The Centre is available for quiet study, with four separate rooms that can be booked for meetings and group work. Amongst its facilities, the Learning Resource Centre has a number of desks, four meeting rooms with large LCD screens, and free use of a photocopier. Also available are core set texts from your module reading lists, and undergraduate and MSc projects from the past two years.

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 and staff to jointly participate in the management and review of the teaching and learning provision.


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/or 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. Applicants are normally invited to attend Post Offer Open Days, which provide the opportunity to talk with and question members of the academic staff.

Candidates must satisfy the general admissions requirements of the University and the entrance requirements for this programme. These are published in full in the University of Exeter Undergraduate Prospectus (see In addition to candidates offering GCE AS and A2, those offering International Baccalaureate, and appropriate VCE A-levels will also be considered, as well as mature candidates with evidence of appropriate alternative qualifications. Direct entry to Stage 2 of the programmes will also be considered for candidates who have successfully completed study equivalent to the core material in the first stage of the programmes.


11. Regulation of Assessment and Academic Standards

Each academic programme in the University is subject to an agreed Faculty 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 Faculty 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 accredited by the Institution of Mathematics and its Applications (IMA) for the purpose of fully meeting the educational requirements of the Chartered Mathematician designation.
Accreditation is awarded for a maximum of 6 years under each assessment exercise. The dates applicable to the current accreditation of this degree programme can be viewed on the IMA list of accredited degrees:
14 Awarding Institution University of Exeter
15 Lead College / Teaching Institution Faculty of Environment, Science and Economy
16 Partner College / Institution
17 Programme accredited/validated by
18 Final Award(s) MSci (Hons)
19 UCAS Code (UG programmes) GG19
20 NQF Level of Final Awards(s): 7
21 Credit (CATS and ECTS) 480 credits (180 ECTS)
22 QAA Subject Benchmarking Group (UG and PGT programmes) Mathematics, Statistics and Operational Research
23 Origin Date February 8th 2023 Last Date of Revision: February 5th 2024