Study information

Data Science (January Start) (2025)

1. Programme Title:

Data Science (January Start)

NQF Level:

7

2. Description of the Programme (as in the Business Approval Form)
The MSc Data Science is an innovative interdisciplinary 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 data science (e.g. statistics, visualisation and machine learning) as well as specific application areas and social context (e.g. governance, ethics, business applications). A research project allows you to develop research skills in an area of interest, guided by a leading academic supervisor.
 
Data science is a growth area with excellent career development potential. University of Exeter is a world-class research active institution which regularly features in UK Top-10 and Global Top-100 rankings. The University is making significant new investment in data science.
 

 

3. Educational Aims of the Programme
The MSc Data Science will provide an outstanding training in data science. The course will introduce students to data science applications and to the mathematical and computational techniques underpinning them. It will also cover machine learning, statistical modelling, tools for handling large and complex datasets, image and text analysis, digital media, and the social and legal context for data analytics.
 
Content will be delivered through a combination of lectures, workshops, individual self-study, and group work on Exeter’s Streatham campus.
 

4. Programme Structure
The MSc Data Science programme is a 1-year full-time or 2-year part time programme of study at National Qualifications Framework (RQF) level 7 (as confirmed against the FHEQ). This programme is divided into ‘stages’. 
 
Each stage is normally equivalent to an academic year. Your 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. You will need to complete a total of 180 credits across your 12-month programme. This will be a mix of mandatory and optional modules, subject to change and timetabling requirements.  
 
Interim / Exit Awards
If you do not complete the programme, you may be able to exit with a lower qualification.
 
A Postgraduate Diploma may be awarded when a student gains at least 120 credits from the compulsory modules.
 
A Postgraduate Certificate may be awarded when a student gains at least 60 credits from the compulsory modules
 

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 University website: 
 
 
You may take optional 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. 
 

 

Stage 1

Code Title Credits Compulsory NonCondonable
165 credits of compulsory modules:
COMM109Programming with Python15YesNo
SOCM033Data Governance and Ethics15YesNo
ECMM443Introduction to Data Science15YesNo
COMM108Data Systems15YesNo
ECMM422Machine Learning15YesNo
MTHM503Applications of Data Science and Statistics15YesNo
ECMM447Social Networks and Text Analysis15YesNo
COMM514Research Project 60YesYes
15 credits of optional modules:
COMM107Algorithms and Architectures 15NoNo
COMM042Introduction to Computer Vision15NoNo

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. Demonstrate knowledge and be able to use methods for data analysis to find patterns and relationships in complex datasets.
  2. Demonstrate knowledge and be able to use methods for statistical inference, data modelling and machine learning.
  3. Show awareness of the social context of data science, including key aspects of data governance, legal requirements, and ethical considerations.
  4. Show awareness of the organisational context of data science, including the role and applications of data science to business practices.
  5. Apply computational methods for analysis of large and complex datasets, including network analysis, image and text analysis, and high-performance computing.
 

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 essays, technical reports, closed book tests, practical exercises in programming and data analysis, project work, and individual and group presentations.
 

B Academic Discipline Core Skills & Knowledge

  1. Critically analyse and interpret relevant academic and technical literature.
  2. Demonstrate competence in underpinning mathematical and computational techniques, including linear algebra, probability, calculus, programming and programming tools such as notebooks and integrated development environments.
  3. Effectively handle large and complex datasets and prepare them for analysis.
  4. Use appropriate methods for data visualisation and presentation of data.
  5. Construct data analysis pipelines to test hypotheses or deliver particular goals.
  6. Use appropriate statistical and machine learning methods to find patterns in complex datasets.
  7. Appreciate the basic legal and regulatory requirements for data privacy, ethical use of data, and data governance.
 

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 essays, technical reports, closed book tests, practical exercises in programming and data analysis, project work, and individual and group presentations.

C Personal / Transferable / Employment Skills & Knowledge

  1. Effectively communicate methods and results based on analysis of complex datasets in both written reports and oral presentations.
  2. Demonstrate awareness of tools and technologies relevant to data science.
  3. Design and manage a data analysis project from initiation to final report.
  4. Work effectively independently or in a team.
 

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 essays, technical reports, closed book tests, practical exercises in programming and data analysis, project work, and individual and group presentations.
 

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. In addition to this, a Pastoral Mentor is also available for all students on this programme to engage with. The Pastoral Mentor can offer further support around wellbeing and academic concerns, and offers regular opportunities for students to engage with them, such as bookable meetings. You can also make an appointment to see individual teaching staff. Student/Staff Liaison Committee enables students & staff to jointly participate in the management and review of the teaching and learning provision.
 
Online Module study resources provide materials for modules that you are registered for, including recording of lectures, as well as 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. 
 
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 department 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 department  and a student handbook is available with relevant information for your studies.
 
The department has dedicated access to two new state-of-the-art computer labs (Lovelace and Babbage), where practical sessions of modules are typically held and that students can also use for self-studying and group work.
 

10. Admission Criteria

Undergraduate applicants must satisfy the Undergraduate Admissions Policy of the University of Exeter.

Postgraduate applicants must satisfy the Postgraduate Admissions Policy of the University of Exeter.

Specific requirements required to enrol on this programme are available at the respective Undergraduate or Postgraduate Study Site webpages.

 

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 N/A
17 Programme accredited/validated by
18 Final Award(s) MSc
19 UCAS Code (UG programmes) PTS1COMCOM06
20 NQF Level of Final Awards(s): 7
21 Credit (CATS and ECTS) 180/90
22 QAA Subject Benchmarking Group (UG and PGT programmes)
23 Origin Date July 4th 2025 Last Date of Revision: October 2nd 2025