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Study information

Data Science (2023)

1. Programme Title:

Data Science

NQF Level:


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

The MSci Data Science is an innovative inter-disciplinary taught course designed with industry and aimed at students wishing to work or research in data science. The course will cover the core areas of mathematics (mathematical methods; probability, statistics and data; statistical modelling and inference) and computer science (programming; object oriented programming; software development; database theory and design). It will also include new modules which will introduce students to applied data science (e.g. machine learning, data structure & algorithm, AI & applications, computational intelligence, HPC, Big Data, Cloud) as well as social context (e.g. governance, ethics, business applications). 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 aims to:

a) Provide a high quality general education in data science comprising a balanced core of key knowledge together with the opportunity to study a range of selected topics in more depth;

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

c) Equip students with knowledge and experience of theoretical and practical data science techniques and practices;

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;

e) Generate in students an enthusiasm for the subject of 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 MSci 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 over 120 credits, you may be awarded a Certificate of Higher Education in Data Science, and if you achieve 240 credits, where at least 90 credits are at NQF Level 5 or above, you may be awarded a Diploma of Higher Education in Data Science.


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 College 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 College 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 Methods30YesYes
MTH1004Probability, Statistics and Data30YesYes
COM1011Fundamentals of Machine Learning15YesNo
ECM1407Social and Professional Issues of the Information Age15YesNo

Stage 2

Code Title Credits Compulsory NonCondonable
ECM2414Software Development15YesNo
ECM2419Database Theory and Design15YesNo
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.


Stage 3

Code Title Credits Compulsory NonCondonable
COM3021Data Science at Scale15YesNo
COM3022Data Science Individual Project 130YesYes
COM3023Machine Learning and AI15YesNo
Select up to 45 credits:
ECM3408Enterprise Computing15NoNo
ECM3412Nature Inspired Computation15NoNo
ECM3422Computability and Complexity 15NoNo
ECM3423Computer Graphics15NoNo
ECM3428Algorithms that Changed the World15NoNo
ECM3446High Performance Computing 15NoNo
MTH3019Mathematics: History and Culture15NoNo
MTH3024Stochastic Processes15NoNo
MTH3028Statistical Inference: Theory and Practice15NoNo
MTH3041Bayesian statistics, Philosophy and Practice15NoNo
MTH3044Bayesian Data Modelling15NoNo
You may select up to 30 credits of other options:
EMP3001Commercial and Industrial Experience15NoNo
*Free choice elective - Up to 30 credits30NoNo

To proceed to Stage 3 of the MSci programme, candidates must normally have achieved a credit weighted average mark in Stage 2 of at least 60%.

Students who do not reach the threshold may progress to stage 3 of the equivalent BSc programme.



Stage 4

Code Title Credits Compulsory NonCondonable
ECMM427Group Development Project 30YesNo
COMM032Data Science MSci Individual Project30YesNo
Select 4 of the following:
COMM511Statistical Data Modelling15NoNo
ECMM410Research Methodology15NoNo
ECMM422Machine Learning15NoNo
ECMM423Evolutionary Computation & Optimisation15NoNo
ECMM424Computer Modelling and Simulation15NoNo
ECMM426Computer Vision15NoNo
MTHM047Bayesian Statistics, Philosophy and Practice 15NoNo
ECMM447Social Networks and Text Analysis15NoNo
MTHM033Statistical Modelling in Space and Time15NoNo

* ECMM446 and COMM511 are available if MTH3012 and MTH3041 have not been taken at Stage 3.

Students may choose up to 30 credits of NQF Level 7 modules which are not listed above, either from within or outside the Faculty of Envronment, Science and Economy, 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)    A range of fundamental concepts and techniques from computer science, mathematics, probability, statistics, machine learning, programming, data science and AI;

2)    The mathematical notations and conventions needed in the analysis of data and computational systems;

3)    The breadth of topics that can be tackled by data science and AI, and 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)    How to use data and methods from data science to answer real world problems in longer projects and how to present results to non-specialists;

6)    The ethics involved in using data and data science.


Learning & Teaching Activities

  • Attending lectures, tutorials and practical workshops;
  • Undertaking project work under supervision, both individually and as part of a team;
  • Completing written exercises;
  • Producing and demonstrating software;
  • Private study.


Assessment Methods

  • Written coursework (ILOs A1-A4, A6);
  • Project report (ILOs A1-A6);
  • Written examination (ILOs A1, A2, A4, A6);
  • Project demonstration (ILOs A1, A3, A4, A5, A6).


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 data science, computer science, mathematics and statistics to unfamiliar problems and demonstrate good selection of choice in solution strategy;

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


Learning & Teaching Activities

  • Attending lectures, tutorials and practical workshops;
  • Undertaking project work under supervision, both individually and as part of a team;
  • Completing written exercises;
  • Producing and demonstrating software;
  • Private study.


Assessment Methods

  • Written coursework (ILOs B1-B7);
  • Project report (ILOs B1, B3, B4, B5, B6, B7);
  • Written examination (ILOs B1-B3, B5, B6, B7);
  • Project demonstration (ILOs B3, B4, B6, B7).


C Personal / Transferable / Employment Skills & Knowledge

1)    Manage a data science project from inception to delivery;

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

3)    Manage time effectively;

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

5)    Work as part of a team;

6)    Plan career and personal development.


Learning & Teaching Activities

  • Attending lectures, tutorials and practical workshops;
  • Undertaking project work under supervision, both individually and as part of a team;
  • Completing written exercises;
  • Producing and demonstrating software;
  • Private study.


Assessment Methods

  • Written coursework (ILOs B2, B3, B4, B5);
  • Project report (ILOs B1-B5);
  • Written examination (ILOs B3);
  • Project demonstration (ILOs C1-C5).


7. Programme Regulations


Integrated Masters Programmes: 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 NQF level 4, and must take at least 210 credits at NQF levels 6 and 7, of which 120 must be at NQF level 7. The pass mark for award of credit is 40% for UG modules (NQF levels 4-6) and 50% for PG modules (NQF 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.

Integrated Masters Programmes: 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

UG Programmes: 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 combined in the ratio 1:2 respectively. Colleges should provide the appropriate ratio for 4 year programmes here. 



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

Undergraduate Degrees                                         Postgraduate Degrees

Class I    70% +                                                        Distinction   70%+

Class II   Division I 60-69%                                       Merit            60-69%

Class II   Division II 50-59%                                      Pass            50-59%

Class III  40-49%


Full details of assessment regulations for UG programmes and PGT programmes assessment regulations 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

Personal and Academic tutoring: It is University policy that all Faculties should have in place a system of academic and personal tutors. The role of academic tutors is to support you on individual modules; 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 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.

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


10. Admission Criteria

(Standard entry) 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. In addition, entry to this programme will normally require a qualification equivalent to A-levels at grades AAA to ABB or higher, including a B in A-level Mathematics.


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).


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) GG17
20 NQF Level of Final Awards(s): 7
21 Credit (CATS and ECTS) 480 (240ECTS)
22 QAA Subject Benchmarking Group (UG and PGT programmes) Computing
23 Origin Date February 8th 2023 Last Date of Revision: February 5th 2024