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Study information

Data Science with Industrial Placement (2025)

1. Programme Title:

Data Science with Industrial Placement

NQF Level:

6

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

The BSc 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 (discrete maths, fundamentals of machine learning and computational maths) 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. Through the provision of extensive practical work experience in a business or commercial setting, contributing the students’ development as experienced computer scientists.

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 BSc Data Science with Industrial Placement programme is a 4 year programme of study at National Qualification Framework (NQF) level 6 (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.

5. Programme Modules

120 credits compulsory modules

Code Title Credits Compulsory NonCondonable
ECM1400Programming15YesYes
ECM1407Social and Professional Issues of the Information Age15YesYes
ECM1410Object-Oriented Programming15YesNo
ECM1413Computers and the Internet15YesNo
ECM1414Data Structures and Algorithms15YesNo
ECM1415Discrete Mathematics for Computer Science15YesNo
ECM1416Computational Mathematics15YesNo
COM1011Fundamentals of Machine Learning15YesNo

Stage 2

Code Title Credits Compulsory NonCondonable
ECM2414Software Development15YesNo
ECM2419Database Theory and Design15YesNo
MTH2006Statistical Modelling and Inference30YesNo
COM2011Machine Learning and Data Science15YesNo
COM2020Team Project15YesNo
SPA2009Data Science in Society15YesNo
ECM2400Employability and Placement Preparation for Computer Scientists0NoNo
Select 15 credits from:
COM2014Computational Intelligence15NoNo
*Free choice elective15NoNo
ECM2423Artificial Intelligence and Applications15NoNo
ECM2427Outside the box: Computer Science Research and Applications15NoNo

120 credit compulsory modules

Code Title Credits Compulsory NonCondonable
ECM3419Industrial Placement120YesYes

75 credits compulsory modules

Code Title Credits Compulsory NonCondonable
COM3021Data Science at Scale15YesNo
ECM3401Individual Literature Review and Project45YesYes
COM3031Probabilistic Machine Learning15YesNo
Select up to 45 credits
ECM3408Enterprise Computing15NoNo
ECM3412Nature Inspired Computation15NoNo
ECM3422Computability and Complexity 15NoNo
COM3024Computer Vision15NoNo
COM3029Social Networks and Text Analysis15NoNo
ECM3446High Performance Computing 15NoNo
MTH3019Mathematics: History and Culture15NoNo
MTH3024Stochastic Processes15NoNo
MTH3028Statistical Inference: Theory and Practice15NoNo
MTH3041Bayesian statistics, Philosophy and Practice15NoNo
MTH3044Bayesian Data Modelling15NoNo
EMP3001Commercial and Industrial Experience15NoNo
*Free choice elective - upto 30 credits30NoNo

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)    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 demons;

7)    Demonstrate a capacity for critical evaluation of argument 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.

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 meant 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, practical workshops.

Undertaking project work under supervision, both individually and as part of a team.

Completing written exercises.

Producing and demonstrating software.

Private study.

(6) is reinforced through individual and group tutorial meetings.

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

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

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 hall 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 Facultyand an extensive range of web-based information (see https://student-harrison.emps.ex.ac.uk/index.php) is maintained for the use of students, including a comprehensive and annually revised student handbook.

The Harrison Learning Resource Centre is general 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

Undergraduate applicants must satisfy the Undergraduate 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 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). There are currently no specific accreditations associated with this programme.

 

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) BSc (Hons)
19 UCAS Code (UG programmes) TEMPCOMP1
20 NQF Level of Final Awards(s): 6
21 Credit (CATS and ECTS) 480/240
22 QAA Subject Benchmarking Group (UG and PGT programmes) Computing
23 Origin Date March 11th 2025 Last Date of Revision: March 13th 2025