Data Science (Professional) (Higher Apprenticeship) (2024)
1. Programme Title:Data Science (Professional) (Higher Apprenticeship) |
NQF Level: |
7 |
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2. Description of the Programme (as in the Business Approval Form) |
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The Non-Integrated Higher Apprenticeship PGDip Data Science (Professional) is an innovative taught programme designed and delivered with industry and targeted at professionals to study alongside employment. The programme delivers the Level 7 Research Scientist Apprenticeship Standard On successful completion of this programme, apprentices will be offered the opportunity to study the associated top-up MSc Data Science (Professional). More information about the standard can be found here (link). The programme combines the academic rigour of Exeter’s long tradition of teaching excellence with the achievement of an industry-recognised professional qualification. The programme will cover the core areas of professional practice as a data scientist (e.g., project management, mentoring and coaching) and core concepts in 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), subject to the optional specialised modules the apprentices choose. |
3. Educational Aims of the Programme |
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The Non-Integrated Higher Apprenticeship PGDip Data Science (Professional) will provide outstanding training in data science tailored to commercial and public sector contexts. Programme 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, using a combination of compulsory and optional modules. Content will be delivered through a combination of face-to-face teaching on campus during 3-day blocks, subsequent weekly online teaching days, incorporating lectures, practicals, seminars and group work, individual self-study, and online interaction with programme tutors. Assessment will primarily be coursework assignments designed to be flexible and fit around other commitments and in-class tests.
This programme will be taught from the Streatham campus and virtually. Industry partners will also contribute to programme design and offer guest lectures and specialist training. This programme format is a first in the UK and represents a leading example of a distinctive industry-focused approach to postgraduate training in data science.
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4. Programme Structure |
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The Non-Integrated Higher Apprenticeship PGDip Data Science (Professional) programme is a two-year programme of study at Regulated Qualifications Framework (RQF) Level 7. It is divided into two stages each consisting of 60 credits.
Mapping to the Level 7 Research Scientist standard |
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 |
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Stage 1: 60 credits of compulsory modules | ||||
COMM414DA | Introduction to Data Science (Professional) | 15 | Yes | No |
COMM415DA | Fundamentals of Data Science (Professional) | 15 | Yes | No |
COMM416DA | Learning From Data (Professional) | 15 | Yes | No |
COMM420DA | Professional Practice 1 | 15 | Yes | No |
Stage 2
Code | Title | Credits | Compulsory | NonCondonable |
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Compulsory Modules (45 Credits) | ||||
COMM412DA | Data in Business and Society | 15 | Yes | No |
COMM421DA | Professional Practice 2 | 15 | Yes | No |
COMM423DA | Work-based Project | 15 | Yes | No |
Optional Modules (Select 15 Credits) | ||||
COMM413DA | Machine Vision | 15 | No | No |
COMM417DA | Machine Learning (Professional) | 15 | No | No |
COMM418DA | Statistical Modelling | 15 | No | No |
COMM419DA | Social Networks and Text Analysis (Professional) | 15 | No | No |
End-Point Assessment | ||||
COMM422DA | Research Scientist: End Point Assessment | 0 | Yes | 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 enhanced knowledge of and 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. Assessment methods will include essays, technical reports, closed book tests, practical exercises in programming and data analysis, project work, and individual and group presentations.. | ||||
B Academic Discipline Core Skills & Knowledge
6) Critically analyse and interpret relevant academic and technical literature. 11) Use appropriate statistical and machine learning methods to find patterns in complex datasets. 12) Appreciate the basic legal and regulatory requirements for data privacy, ethical use of data, data governance, and Research Scientist Standard | 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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Award You must attempt the End-Point Assessment (EPA) before the University of Exeter award can be conferred.
Progression Following successful completion of the PGDip, you will be permitted to apply to the MSc Data Science (Professional) programme. 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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In accordance with University policy a system of Academic mentors 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 tripartite meetings with your academic mentor; you may request additional meetings as and when required. The role of Academic mentor is to provide you with advice and support during the programme study. You academic mentor will work with you to address questions, explain processes, monitor progress, and sign off your end point assessment materials. They will help sign-post to module academic leads and welfare and other University services if you need. They will also conduct face-to-face tripartite meetings at your workplace sites and virtual meetings using webinars and other technology. You can also make an appointment to see individual teaching staff.
Additionally, the College 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 College and an extensive range of web-based information (see https://intranet.exeter.ac.uk/emps/) is maintained for the use of students, including a comprehensive and annually revised student handbook.
The Harrison Learning Resource Centre is generally 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 set texts from your module reading lists, and undergraduate and MSc projects from the past two years. |
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. The Degree Apprenticeship end point assessment will be performed by an end point assessment organisation following the assessment plan from the apprenticeship standard -st0759_research-scientist_l7_-adjustment-031121-2.pdf (instituteforapprenticeships.org)
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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). |
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 | Institute for Apprenticeships and Technical Education (iFaTE), EPAO | |
18 | Final Award(s) | PGDip | |
19 | UCAS Code (UG programmes) | DSP_PGDIP | |
20 | NQF Level of Final Awards(s): | 7 | |
21 | Credit (CATS and ECTS) | 120 credits (60 ECTS) | |
22 | QAA Subject Benchmarking Group (UG and PGT programmes) | Research Scientist Apprenticeship Standard |
23 | Origin Date | July 11th 2024 | Last Date of Revision: | July 11th 2024 |
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