Data Science (Professional) (2019)
1. Programme Title:Data Science (Professional) |
NQF Level: |
7 |
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2. Description of the Programme (as in the Business Approval Form) |
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The MSc Data Science (Professional) is an innovative taught course designed and delivered with industry and targeted at professionals who are unable to study full-time. The course will cover the core areas of data science (e.g. machine learning, statistics) as well as underpinning tools (e.g. programming, mathematics), specific applications (e.g. network analysis, text analysis, machine vision) and social context (e.g. governance, ethics, business applications). This programme is available for open registrations but is primarily used to deliver the academic content for a Level 7 Apprenticeship, currently using the approved standard for “Research Scientist”. More information about this standard can be found here. A mapping of the knowledge, skills and behaviours associated with this standard to the modules within this programme is given below. Data science is a growth area with excellent career development potential. Students will benefit from contact with leading academics and gain a respected qualification alongside their current employment. Employers of students participating in the course will gain valuable knowledge and improve the skills base within their organisation, without losing productivity. 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 |
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The MSc Data Science (Professional) will provide outstanding training in data science tailored to commercial and public sector contexts. Course content will cover the fundamental mathematical and computational techniques underpinning data science applications, with extensive coverage of machine learning and 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 intensive “block” teaching (e.g. 3-day residentials incorporating lectures, practicals, seminars and group work), individual self-study, and online interaction with course tutors. Assessment will primarily be coursework assignments designed to fit around other commitments. This programme will be taught at both the Streatham campus of the University of Exeter and chosen locations in central London, with the possibility of some blocks taught offsite at the facilities of relevant data science organisations. Two cohorts (Exeter and London) will receive identical delivery, with the aim of making the programme accessible to wider range of students and geographical areas. A staggered teaching schedule will be used to achieve this mirrored delivery across different locations (e.g. each 3-day teaching block will be repeated the following week in the other location).Industry partners will also contribute to course design and offer guest lectures and specialist training. This course format is currently unique in the UK and represents a distinctive industry-focused approach to postgraduate training in data science. |
4. Programme Structure |
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The MSc Data Science (Professional) programme is a 2 year programme of study at National Qualification Framework (NQF) Level 7 (as confirmed against the FHEQ). This programme is divided into 2 ‘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.
Mapping to the Level 7 Research Scientist standard When used to deliver the academic content needed for this apprenticeship, the knowledge, skills and behaviours (KSBs) associated with the apprenticeship have been mapped to programme modules.' |
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 programme review of this programme. Details of the modules currently offered may be obtained from the College web site: http://intranet.exeter.ac.uk/emps/studentinfo/subjects/computerscience/modules/
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Stage 1
| Code | Title | Credits | Compulsory | NonCondonable |
|---|---|---|---|---|
| Compulsory Modules: | ||||
| ECMM455 | Introduction to Data Science (Professional) | 15 | Yes | No |
| ECMM456 | Fundamentals of Data Science (Professional) | 15 | Yes | No |
| ECMM457 | Learning From Data (Professional) | 15 | Yes | No |
| ECMM432 | Data in Business and Society | 15 | Yes | No |
| ECMM433 | Project 1 | 30 | Yes | Yes |
Stage 2
| Code | Title | Credits | Compulsory | NonCondonable |
|---|---|---|---|---|
| Compulsory Modules | ||||
| ECMM458 | Machine Learning (Professional) | 15 | Yes | No |
| ECMM459 | Statistical Modelling | 15 | Yes | No |
| ECMM435 | Project 2 | 30 | Yes | Yes |
| Choose 2 modules from the options below. | ||||
| ECMM439 | Social Networks and Text Analysis | 15 | No | No |
| ECMM440 | High Performance Computing and Data Architectures | 15 | No | No |
| ECMM441 | Machine Vision | 15 | No | No |
| ECMM442 | Information Security | 15 | 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) Demonstrate knowledge and be able to use methods for machine learning to find patterns and relationships in complex datasets. | Learning & Teaching ActivitiesLectures, 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. | ||||
B Academic Discipline Core Skills & Knowledge
6) Critically analyse and interpret relevant academic and technical literature. | Learning & Teaching ActivitiesLectures, 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. | ||||
C Personal / Transferable / Employment Skills & Knowledge
13) Effectively communicate methods and results based on analysis of complex datasets in both written reports and oral presentations. | Learning & Teaching ActivitiesLectures, 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. | ||||
7. Programme Regulations |
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The programme consists of 180 credits with 90 credits taken at each stage where the programme is offered part time over 2 years and 60 credits taken at each stage where the programme is offered part time over 3 years. In total, participants must take at least 180 credits at NQF level 7. The pass mark for award of credit in PG modules (NQF level 7) is 50%. |
8. College Support for Students and Students' Learning |
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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. You can also make an appointment to see individual teaching staff. |
10. Admission Criteria |
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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. Entry requirements for this programme can be found on the Postgraduate Study Page. Candidates must satisfy the general admissions requirements and English Language requirements of the University of Exeter. |
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). |
| 14 | Awarding Institution | University of Exeter | |
|---|---|---|---|
| 15 | Lead College / Teaching Institution | College of Engineering, Mathematics and Physical Sciences | |
| 16 | Partner College / Institution | ||
| 17 | Programme accredited/validated by | ||
| 18 | Final Award(s) | MSc | |
| 19 | UCAS Code (UG programmes) | DSP | |
| 20 | NQF Level of Final Awards(s): | 7 | |
| 21 | Credit (CATS and ECTS) | 180 credits (90 ECTS) | |
| 22 | QAA Subject Benchmarking Group (UG and PGT programmes) | Data Science | |
| 23 | Origin Date | July 10th 2018 | Last Date of Revision: | September 10th 2019 |
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