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Study information

Data Science (Professional) (Higher Apprenticeship) (2024)

1. Programme Title:

Data Science (Professional) (Higher Apprenticeship)

NQF Level:

7

2. Description of the Programme (as in the Business Approval Form)

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. 

Teaching on the programme will be centred around three-day face-to-face teaching blocks delivered on campus at University of Exeter  at the start of each term. These teaching blocks will then be followed by a day per week of distance learning for the remainder of the term and  professional practice training sessions which comprise both face-to-face and online teaching throughout the programme. The weekly distance learning sessions will be comprised of live content delivered virtually alongside independent study.

Assessment where possible will be focused on your organisational context including a work-based project at the end of the programme. You must attempt the End-Point Assessment which will assess your training outcomes. Industry experts are regularly consulted on programme content and will sometimes act as guest lecturers, ensuring relevance to modern business needs. 

Data science is a growth area with excellent career development potential. You will benefit from contact with leading academics and gain a respected qualification alongside your current employment. Employers of students participating in the programme 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-150 rankings. The University is making significant new investment in data science.

3. Educational Aims of the Programme

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.

 

4. Programme Structure

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

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

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/

 

Stage 1

Code Title Credits Compulsory NonCondonable
Stage 1: 60 credits of compulsory modules
COMM414DAIntroduction to Data Science (Professional)15YesNo
COMM415DAFundamentals of Data Science (Professional)15YesNo
COMM416DALearning From Data (Professional)15YesNo
COMM420DAProfessional Practice 115YesNo

Stage 2

Code Title Credits Compulsory NonCondonable
Compulsory Modules (45 Credits)
COMM412DAData in Business and Society15YesNo
COMM421DAProfessional Practice 215YesNo
COMM423DAWork-based Project15YesNo
Optional Modules (Select 15 Credits)
COMM413DAMachine Vision15NoNo
COMM417DAMachine Learning (Professional)15NoNo
COMM418DAStatistical Modelling15NoNo
COMM419DASocial Networks and Text Analysis (Professional)15NoNo
End-Point Assessment
COMM422DAResearch Scientist: End Point Assessment0YesNo

6. Programme Outcomes Linked to Teaching, Learning & Assessment Methods

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.

2) Demonstrate enhanced knowledge of and use methods for statistical modelling.

3) Describe in detail the social context of data science, including key aspects of data governance, legal requirements, and ethical considerations.

4) Explain in detail the organisational context of data science, including the role and applications of data science to business practices.

5) Apply with limited guidance computational methods for analysis of large and complex datasets, including network analysis, and image and text analysis.

Learning & Teaching Activities

Lectures, 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.

7) Demonstrate competence in underpinning mathematical and computational techniques, including linear algebra, probability, programming and programming tools.

8) Effectively handle large and complex datasets and prepare them for analysis.

9) Use appropriate methods for data visualisation and presentation of data.

10) Construct data analysis pipelines to test hypotheses or deliver particular goals.

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 Activities

Lectures, 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.

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.

14) Demonstrate awareness of tools and technologies relevant to data science.

15) Design and manage a data analysis project from initiation to final report.

16) Work effectively independently or in a team.

Learning & Teaching Activities

Lectures, 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.

7. Programme Regulations

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.

 

8. College Support for Students and Students' Learning

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.


Information Technology (IT) Services provide a wide range of services throughout the Exeter campuses including open access computer rooms, some of which are available 24 hours, 7 days a week.  Help may be obtained through the Helpdesk, and most study bedrooms in halls and flats are linked to the University’s campus network.

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.

Online Module study resources provide materials for modules that you are registered for, in addition to some useful subject and IT resources. Generic study support resources, library and research skills, past exam papers, and the ‘Academic Honesty and Plagiarism’ module are also available through the student portal (http://vle.exeter.ac.uk).

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

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

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)


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

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 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