Data Science (Professional) (2024)
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 a taught programme targeted at professionals who want to study alongside employment and are completing, or have completed, the Non-Integrated Higher Apprenticeship PGDip Data Science (Professional) programme. 120 credits from the PGDip programme will be accredited towards the MSc Data Science (Professional) using the University’s regulations governing Accreditation of Prior Learning which requires 180 credits in total for the MSc award. |
3. Educational Aims of the Programme |
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The MSc Data Science (Professional) learning outcomes include; the application of research-led techniques in data science, understanding of the fundamental mathematical and computational techniques underpinning data science applications, with extensive coverage of machine learning and statistical modelling, practical skills with tools for handling large and complex datasets, understanding of image and text analysis, digital media, and the social and understanding of the legal context for data analytics. This programme will be taught from the Streatham campus of the University of Exeter virtually. This programme 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 one-year programme of study at Regulated Qualifications Framework (RQF) Level 7 assuming completion of the two-year PGDip and 120 credits from recognition of prior learning. The MSc award comprises a single additional stage. |
5. Programme Modules |
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Stage 1: 60 credits of compulsory modules
Code | Title | Credits | Compulsory | NonCondonable |
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COMM424DA | Individual Research Project | 60 | Yes | Yes |
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 enhanced knowledge of and use methods for machine learning to find patterns and relationships in complex datasets. | Learning & Teaching ActivitiesThe learning and teaching activities include individual literature study, data analysis, supervision and discussion, and coding exercise. | |||
Assessment MethodsAssessment methods will include essays, project work, and individual presentations. | ||||
B Academic Discipline Core Skills & Knowledge
6) Critically analyse and interpret relevant academic and technical literature. | Learning & Teaching ActivitiesThe learning and teaching activities include individual literature study, data analysis, supervision and discussion, and coding exercise. | |||
Assessment MethodsAssessment methods will include essays, project work, and individual presentations. | ||||
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 ActivitiesThe learning and teaching activities include individual literature study, data analysis, supervision and discussion, and coding exercise. | |||
Assessment MethodsAssessment methods will include essays, project work, and individual presentations. |
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.
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8. College Support for Students and Students' Learning |
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A University-wide statement on student learning 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 (also your project supervisor); 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. Student/Staff Liaison Committee enables students and staff to jointly participate in the management and review of the teaching and learning provision. |
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. |
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. |
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 | |
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15 | Lead College / Teaching Institution | College of Engineering, Mathematics and Physical Sciences | |
16 | Partner College / Institution | N/A | |
17 | Programme accredited/validated by | N/A | |
18 | Final Award(s) | MSc | |
19 | UCAS Code (UG programmes) | N/A | |
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) | N/A |
23 | Origin Date | June 27th 2022 | Last Date of Revision: | June 27th 2022 |
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