Data Science (2024)
1. Programme Title:Data Science |
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.
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3. Educational Aims of the Programme |
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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.
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4. Programme Structure |
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Your BSc Data Science programme is a 3 year programme of study at National Qualification Framework (NQF) level 6 (as confirmed against the FHEQ). This programme is divided into 3 ‘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.
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5. Programme Modules |
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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: https://intranet.exeter.ac.uk/emps/ 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: https://intranet.exeter.ac.uk/emps/ You may take Elective Modules outside of the programme of up to 15 credits in the second stage and 30 credits in the third stage 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.
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120 credits compulsory modules
Code | Title | Credits | Compulsory | NonCondonable |
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ECM1400 | Programming | 15 | Yes | Yes |
ECM1410 | Object-Oriented Programming | 15 | Yes | Yes |
COM1011 | Fundamentals of Machine Learning | 15 | Yes | No |
ECM1407 | Social and Professional Issues of the Information Age | 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 |
Stage 2
Code | Title | Credits | Compulsory | NonCondonable |
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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 |
ECM2434 | Group Software Engineering Project | 15 | No | No |
Select 30 credits from: | ||||
COM2014 | Computational Intelligence | 15 | No | No |
* | Free choice elective | 15 | No | No |
Stage 3
Code | Title | Credits | Compulsory | NonCondonable |
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COM3021 | Data Science at Scale | 15 | Yes | No |
ECM3401 | Individual Literature Review and Project | 45 | Yes | Yes |
COM3023 | Machine Learning and AI | 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 |
ECM3423 | Computer Graphics | 15 | No | No |
ECM3428 | Algorithms that Changed the World | 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 |
You may select up to 30 credits of other options: | ||||
EMP3001 | Commercial and Industrial Experience | 15 | No | No |
* | Free choice elective - up to 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 & KnowledgeDemonstrate 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.
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Assessment Methods
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B Academic Discipline Core Skills & Knowledge1) 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.
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Assessment Methods
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C Personal / Transferable / Employment Skills & Knowledge1) 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.
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Assessment Methods
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7. Programme Regulations |
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Credit The programme consists of 360 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 Stage 1, and must take at least 90 credits at Stage 3. The pass mark for award of credit in an invidual module is 40%. Progression You can progress to the next stage (or the final year, to proceed to the award of an honours degree) once at least 90 credits have been passed in a stage, and provided that an average of at least 40% has been achieved over the 120 credits of assessment for that stage. 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. You must have registered for and participated in modules amounting to at least 120 credits in the stage. You must pass the modules marked with a 'Yes' in the 'non-condonable' column in the tables above. 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. Classification 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.
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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.
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10. Admission Criteria |
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(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 ABB to AAA or higher, including A-level Mathematics.
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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 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.
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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).
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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) | GG16 | |
20 | NQF Level of Final Awards(s): | 6 | |
21 | Credit (CATS and ECTS) | 360 (180 ECTS) | |
22 | QAA Subject Benchmarking Group (UG and PGT programmes) | Computing |
23 | Origin Date | October 19th 2023 | Last Date of Revision: | August 6th 2024 |
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