Data Science with Industrial Placement (2025)
1. Programme Title:Data Science with Industrial Placement |
NQF Level: |
6 |
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2. Description of the Programme (as in the Business Approval Form) |
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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 |
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The programme aims to: |
4. Programme Structure |
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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 |
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120 credits compulsory modules
| Code | Title | Credits | Compulsory | NonCondonable |
|---|---|---|---|---|
| ECM1400 | Programming | 15 | Yes | Yes |
| ECM1407 | Social and Professional Issues of the Information Age | 15 | Yes | Yes |
| ECM1410 | Object-Oriented Programming | 15 | Yes | No |
| ECM1413 | Computers and the Internet | 15 | Yes | No |
| ECM1414 | Data Structures and Algorithms | 15 | Yes | No |
| ECM1415 | Discrete Mathematics for Computer Science | 15 | Yes | No |
| ECM1416 | Computational Mathematics | 15 | Yes | No |
| COM1011 | Fundamentals of Machine Learning | 15 | Yes | No |
Stage 2
| Code | Title | Credits | Compulsory | NonCondonable |
|---|---|---|---|---|
| ECM2414 | Software Development | 15 | Yes | No |
| ECM2419 | Database Theory and Design | 15 | Yes | No |
| MTH2006 | Statistical Modelling and Inference | 30 | Yes | No |
| COM2011 | Machine Learning and Data Science | 15 | Yes | No |
| COM2020 | Team Project | 15 | Yes | No |
| SPA2009 | Data Science in Society | 15 | Yes | No |
| ECM2400 | Employability and Placement Preparation for Computer Scientists | 0 | No | No |
| Select 15 credits from: | ||||
| COM2014 | Computational Intelligence | 15 | No | No |
| * | Free choice elective | 15 | No | No |
| ECM2423 | Artificial Intelligence and Applications | 15 | No | No |
| ECM2427 | Outside the box: Computer Science Research and Applications | 15 | No | No |
120 credit compulsory modules
| Code | Title | Credits | Compulsory | NonCondonable |
|---|---|---|---|---|
| ECM3419 | Industrial Placement | 120 | Yes | Yes |
75 credits compulsory modules
| Code | Title | Credits | Compulsory | NonCondonable |
|---|---|---|---|---|
| COM3021 | Data Science at Scale | 15 | Yes | No |
| ECM3401 | Individual Literature Review and Project | 45 | Yes | Yes |
| COM3031 | Probabilistic Machine Learning | 15 | Yes | No |
| Select up to 45 credits | ||||
| ECM3408 | Enterprise Computing | 15 | No | No |
| ECM3412 | Nature Inspired Computation | 15 | No | No |
| ECM3422 | Computability and Complexity | 15 | No | No |
| COM3024 | Computer Vision | 15 | No | No |
| COM3029 | Social Networks and Text Analysis | 15 | No | No |
| ECM3446 | High Performance Computing | 15 | No | No |
| MTH3019 | Mathematics: History and Culture | 15 | No | No |
| MTH3024 | Stochastic Processes | 15 | No | No |
| MTH3028 | Statistical Inference: Theory and Practice | 15 | No | No |
| MTH3041 | Bayesian statistics, Philosophy and Practice | 15 | No | No |
| MTH3044 | Bayesian Data Modelling | 15 | No | No |
| EMP3001 | Commercial and Industrial Experience | 15 | No | No |
| * | Free choice elective - upto 30 credits | 30 | No | No |
6. Programme Outcomes Linked to Teaching, Learning & Assessment Methods |
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| 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; | Learning & Teaching Activities
Attending lectures, tutorials and practical workshops. | |||
Assessment Methods
Written coursework (ILOs A1-A4, A6) | ||||
B Academic Discipline Core Skills & Knowledge
1) Think logically; | Learning & Teaching Activities
Attending lectures, tutorials and practical workshops. | |||
Assessment Methods
Written coursework (ILOs B1-B7) | ||||
C Personal / Transferable / Employment Skills & Knowledge
1) Manage a data science project from inception to delivery; | Learning & Teaching Activities
Attending lectures, tutorials, practical workshops. | |||
Assessment Methods
Written coursework (ILOs B2, B3, B4, B5) | ||||
7. Programme Regulations |
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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 |
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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. |
10. Admission Criteria |
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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.
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11. Regulation of Assessment and Academic Standards |
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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 |
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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.
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| 14 | Awarding Institution | University of Exeter | |
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| 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 |
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