Programme Information | Study information

1. Programme Title: Data Science (Professional) (Higher Apprenticeship) (January)	NQF Level:	7

2. Description of the Programme (as in the Business Approval Form)
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 programme delivers the Level 7 Research Scientist Apprenticeship Standard, against which we have mapped our MSc Data Science (Professional) degree. More information about this standard can be found here. The programme combines the academic rigour of Exeter’s long tradition of teaching excellence with the achievement of an industry-recognised professional qualification. 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), subject to the optional specialised modules the apprentices choose. Three day face to face teaching blocks delivered on campus at University of Exeter will begin each term. These teaching blocks will then be completed by a day a week distance learning sessions for the remainder of the year. The weekly distance learning sessions will be comprised of live content delivered virtually alongside independent study. Assessment where possible will be focused on their organisational context. In their final year on the programme Students will undertake one substantial research project based in their organisation which will be closely supported by academics with relevant expertise. Industry experts are regularly consulted on course 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. 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-150 rankings. The University is making significant new investment in data science.

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

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 programme delivers the Level 7 Research Scientist Apprenticeship Standard, against which we have mapped our MSc Data Science (Professional) degree. More information about this standard can be found here. The programme combines the academic rigour of Exeter’s long tradition of teaching excellence with the achievement of an industry-recognised professional qualification. 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), subject to the optional specialised modules the apprentices choose.

Three day face to face teaching blocks delivered on campus at University of Exeter will begin each term. These teaching blocks will then be completed by a day a week distance learning sessions for the remainder of the year. The weekly distance learning sessions will be comprised of live content delivered virtually alongside independent study.

Assessment where possible will be focused on their organisational context. In their final year on the programme Students will undertake one substantial research project based in their organisation which will be closely supported by academics with relevant expertise. Industry experts are regularly consulted on course 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. 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-150 rankings. The University is making significant new investment in data science.

3. Educational Aims of the Programme
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 , using a combination of core and optional modules. Content will be delivered through a combination of intensive teaching (e.g. a block of 3-day face to face on campus teaching and subsequent weekly online teaching, 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 and in class tests. This programme will be taught at the Streatham campus of the University of Exeter and virtually, with the possibility of termly ‘meet ups’ in London for apprentices who are located in that region. 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.

3. Educational Aims of the Programme

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 , using a combination of core and optional modules. Content will be delivered through a combination of intensive teaching (e.g. a block of 3-day face to face on campus teaching and subsequent weekly online teaching, 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 and in class tests.

This programme will be taught at the Streatham campus of the University of Exeter and virtually, with the possibility of termly ‘meet ups’ in London for apprentices who are located in that region. 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
The MSc Data Science (Professional) programme is a 3 year programme of study at National Qualification Framework (NQF) Level 7 (as confirmed against the FHEQ). It is divided into 3 ‘Stages’, with each Stage normally being 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. The three Stages will each consist of 60 credits to give 180 credits in total. The Level 7 Research Scientist standard shall be delivered within the first two years of the programme. Over the first two year period, apprentices will accumulate 120 credits which is equivalent to a Postgraduate Diploma qualification. The 120 credits are achieved via taught modules and guided independent study. Apprenticeship funding guidelines are only applicable for the first two years of study. Apprentices will use the third and final year of the programme to undertake a Research Project which will enable them to achieve the 180 credits required for a full Masters degree qualification. Apprentices can achieve a Level 7 Research Scientist award and Post Graduate Diploma of Education over 2 years through: • One x3 day on campus teaching block each Term (September in Term 1, January in Term 2 and May in Term 3) • a day a week distance learning in Terms 1, 2 and 3, and off the job programme engagement over the summer period. Apprentices can achieve a MSc Data Science degree in year 3 through; • undertaking a research project to apply the knowledge learnt in the first two years Interim Awards If you do not complete the programme you may be able to exit with a lower qualification: - Postgraduate Diploma: At least 120 credits of which 90 or more must be at level M. - Postgraduate Certificate: At least 60 credits of which 45 or more must be at level M. 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.

4. Programme Structure

The MSc Data Science (Professional) programme is a 3 year programme of study at National Qualification Framework (NQF) Level 7 (as confirmed against the FHEQ). It is divided into 3 ‘Stages’, with each Stage normally being 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. The three Stages will each consist of 60 credits to give 180 credits in total.

The Level 7 Research Scientist standard shall be delivered within the first two years of the programme. Over the first two year period, apprentices will accumulate 120 credits which is equivalent to a Postgraduate Diploma qualification. The 120 credits are achieved via taught modules and guided independent study. Apprenticeship funding guidelines are only applicable for the first two years of study. Apprentices will use the third and final year of the programme to undertake a Research Project which will enable them to achieve the 180 credits required for a full Masters degree qualification.

Apprentices can achieve a Level 7 Research Scientist award and Post Graduate Diploma of Education over 2 years through:

• One x3 day on campus teaching block each Term (September in Term 1, January in Term 2 and May in Term 3)

• a day a week distance learning in Terms 1, 2 and 3, and off the job programme engagement over the summer period.

Apprentices can achieve a MSc Data Science degree in year 3 through;

• undertaking a research project to apply the knowledge learnt in the first two years

Interim Awards

If you do not complete the programme you may be able to exit with a lower qualification:

- Postgraduate Diploma: At least 120 credits of which 90 or more must be at level M.

- Postgraduate Certificate: At least 60 credits of which 45 or more must be at level M.

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/

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
COMM414DA	Introduction to Data Science (Professional)	15	Yes	No
COMM415DA	Fundamentals of Data Science (Professional)	15	Yes	No
COMM416DAJ	Learning From Data (Professional)	15	Yes	No
COMM420DAJ	Professional Practice 1	15	Yes	No

Stage 2

Code	Title	Credits	Compulsory	NonCondonable
Compulsory Modules (45 Credits)
COMM412DA	Data in Business and Society	15	Yes	No
COMM421DAJ	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

Stage 3

Code	Title	Credits	Compulsory	NonCondonable
COMM424DA	Individual Research Project	60	Yes	Yes
COMM422DAJ	Research Scientist: End Point Assessment	0	Yes	Yes

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 knowledge and be able to use methods for machine learning to find patterns and relationships in complex datasets. 2) Demonstrate knowledge and be able to use methods for statistical modelling. 3) Show awareness of the social context of data science, including key aspects of data governance, legal requirements, and ethical considerations. 4) Show awareness of the organisational context of data science, including the role and applications of data science to business practices. 5) Apply computational methods for analysis of large and complex datasets, including network analysis, image and text analysis, and high-performance computing.	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, and data governance.	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.

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 knowledge and be able to use methods for machine learning to find patterns and relationships in complex datasets.

2) Demonstrate knowledge and be able to use methods for statistical modelling.

3) Show awareness of the social context of data science, including key aspects of data governance, legal requirements, and ethical considerations.

4) Show awareness of the organisational context of data science, including the role and applications of data science to business practices.

5) Apply computational methods for analysis of large and complex datasets, including network analysis, image and text analysis, and high-performance computing.

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, and data governance.

Learning & Teaching Activities

Assessment Methods

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

Assessment Methods

7. Programme Regulations
The programme consists of 180 credits with 60 credits taken at each stage 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%. Progression 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. Up to (45/30/20) credits of failure can be condoned on the following conditions: a. You must have completed and been assessed in modules amounting to sufficient credit for the final award (i.e. 180 credits for a Masters; 120 credits for a PGDip; and 60 credits for a PGCert). b. You must pass the modules marked with a ‘Yes’ in the ‘non-condonable’ column in the tables above. c. You must achieve an average mark of at least 50% across the full 60 or 90 credits of assessment in the stage, including any failed and condoned modules. d. Condonement can only be applied to failed modules which have a mark of 0–49%. Condonement will be considered at the end of the programme based on all module results but any non-condonable fails will be referred or deferred immediately. Assessment and Awards The award will normally be based on at least 180 credits of which 150 or more must be at NQF level 7 Classification The marking of modules and the classification of awards broadly corresponds to the following percentage marks: Postgraduate Degrees Distinction 70%+ Merit 60-69% Pass 50-59% Full details of 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.

7. Programme Regulations

The programme consists of 180 credits with 60 credits taken at each stage 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%.

Progression

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.

Up to (45/30/20) credits of failure can be condoned on the following conditions:

a. You must have completed and been assessed in modules amounting to sufficient credit for the final award (i.e. 180 credits for a Masters; 120 credits for a PGDip; and 60 credits for a PGCert).

b. You must pass the modules marked with a ‘Yes’ in the ‘non-condonable’ column in the tables above.

c. You must achieve an average mark of at least 50% across the full 60 or 90 credits of assessment in the stage, including any failed and condoned modules.

d. Condonement can only be applied to failed modules which have a mark of 0–49%.

Condonement will be considered at the end of the programme based on all module results but any non-condonable fails will be referred or deferred immediately.

Assessment and Awards

The award will normally be based on at least 180 credits of which 150 or more must be at NQF level 7

Classification

The marking of modules and the classification of awards broadly corresponds to the following percentage marks:

Postgraduate Degrees

Distinction 70%+

Merit 60-69%

Pass 50-59%

Full details of 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.

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 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. 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 & staff to jointly participate in the management and review of the teaching and learning provision.

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 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.

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 & 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.

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 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.

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 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
17	Programme accredited/validated by
18	Final Award(s)	MSc
19	UCAS Code (UG programmes)	DSPJan
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	February 8th 2023	Last Date of Revision:	February 8th 2023

Data Science (Professional) (Higher Apprenticeship) (January) (2023)

1. Programme Title:

Data Science (Professional) (Higher Apprenticeship) (January)

NQF Level:

7

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

3. Educational Aims of the Programme

4. Programme Structure

5. Programme Modules

Stage 1

Stage 2

Stage 3

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

A Specialised Subject Skills & Knowledge

Learning & Teaching Activities

Assessment Methods

B Academic Discipline Core Skills & Knowledge

Learning & Teaching Activities

Assessment Methods

C Personal / Transferable / Employment Skills & Knowledge

Learning & Teaching Activities

Assessment Methods

7. Programme Regulations

8. College Support for Students and Students' Learning

10. Admission Criteria

11. Regulation of Assessment and Academic Standards

12. Indicators of Quality and Standards