programme specification msc digital and technology ...€¦ · msc digital and technology solutions...

DTSS (Data Analytics) Apprenticeship

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PROGRAMME SPECIFICATION MSc Digital and Technology Solutions Specialist (Data Analytics) Master’s Degree Apprenticeship

Final

PART 1: COURSE SUMMARY INFORMATION

Course summary

Final award MSc Data Analytics

Intermediate award Postgraduate Certificate in Data Analytics

Postgraduate Diploma in Data Analytics

Course status Validated

Awarding body University of Brighton

School Computing, Engineering and Mathematics

Location of study/ campus Moulsecoomb

Partner institution(s)

Name of institution Host department Course status

1. N/A SELECT

Admissions

Admissions agency Direct to School


Entry requirements Include any progression opportunities into the course.

Individual employers will set the selection criteria as per the apprenticeship standard, however these will need to meet or exceed the university requirements:

Route A entry requirements:

either a 2:1 or higher degree in any subject (or equivalent experience) and some knowledge and skills in data analysis, statistics and programming (through work experience, online courses, self-study, etc.);

or a 2:2 or higher degree in a relevant subject such as mathematics, statistics, data mining or artificial intelligence (or equivalent experience).

Route B entry requirements:

a 2:1 or higher degree in a relevant subject such as mathematics, statistics, data mining or artificial intelligence (or equivalent experience).

Applicants will be expected to hold and evidence Level 2 qualifications in English and Maths. Apprentices accepted without level 2 English or Maths will be required to study them as part of the apprenticeship programme.

The apprentice must meet the apprenticeship eligibility criteria (including residency status, right to work, previous qualifications etc.)

The apprentice must be employed in an appropriate work setting on a contract at least the duration of the apprenticeship and sponsored by their employer.

If English is not the applicant's first language: IELTS 6.5 overall with 6.0 in writing, or equivalent.

Recognised Prior Learning such as professional qualifications and / or relevant work experience will also be considered. Individual offers may vary. Applicants may be interviewed by course team members if necessary.

Route A and Route B apprentices can start in September.

Route B apprentices can also start in January.

Exceptionally, Route A apprentices can start in January if the Course Leader confirms that their Recognised Prior Learning allows them to do so.

Start date (mmm-yy) Normally September

Sept-19 and Feb-20

Mode of study

Mode of study Duration of study (standard) Maximum registration period

Full-time Select

Part-time 2 years 6 years

Sandwich Select Select

Distance Select Select

Course codes/categories

UCAS code N/A


Contacts

Course Leader (or Course Development Leader)

Dr Sónia Timoteo Inácio

Admissions Tutor Dr Sónia Timoteo Inácio

Examination and Assessment

External Examiner(s)

Name Place of work Date tenure expires

Dr Michaela Cottee Principal Lecturer, Statistics, University of Hertfordshire

31/12/2019

Examination Board(s) (AEB/CEB)

Postgraduate Computing, Engineering and Mathematical Sciences AEB / CEB

Approval and review

Approval date Review date

Validation December 20181 Next review date 20242

Programme Specification June 20193 June 20204

Professional, Statutory and Regulatory Body 1 (if applicable):

5



1 Date of original validation. 2 Date of most recent periodic review (normally academic year of validation + 5 years). 3 Month and year this version of the programme specification was approved (normally September). 4 Date programme specification will be reviewed (normally approval date + 1 year). If programme specification is applicable to a particular cohort, please state here. 5 Date of most recent review by accrediting/ approving external body.


PART 2: COURSE DETAILS

AIMS AND LEARNING OUTCOMES

Aims

The aims of the course are:

The overall aim of the course is to provide apprentices with a unique combination of mathematical and statistical data analytics skills. These skills will develop in tandem with the skills necessary within complex data specific projects, to oversee and manage; critically appraise; assess feasibility; risk analyse; present and see projects through to completion.

This course meets the requirements of the apprenticeship standard Digital and Technology Solutions specialist (Data Analytics) and therefore aims to meet head-on the statistical, computational and business analytical requirements of the burgeoning Data Science industry by producing graduates with well-rounded skills and expertise in specialist data analytics and associated software, quantitative methods and techniques, business intelligence and who are able to assess project viability, manage large data projects successfully and lead data-driven transformations of business practice. More specifically, the course aims to meet industrial, commercial, academic and public sector requirements for graduate employment in an analytics capacity by

producing graduates with the ability to derive information requirements relating to strategic imperatives, and who have the capacity to recommend analytic approaches suitable for specific data analysis and can design and test analytics solutions using a range of potential software platforms;

equipping apprentices with the mathematical and statistical knowledge specific to a data analytics environment, and hence enhancing the apprentice’s ability to implement appropriate quantitative analyses to large data projects.

developing the apprentices’ ability to construct, present and defend the business and financial case for the implementation of an analytics project.

furnishing apprentices with, and further developing their decision making, project management and leadership skills appropriate to an analytics environment.

Learning outcomes

The outcomes of the main award provide information about how the primary aims are demonstrated by apprentices following the course. These are mapped to external reference points where appropriate6.

Knowledge and theory On successful completion of the course the graduate should be able to:

LO1. Critically evaluate the requirements for the solution to an analytics problem in terms of data structures and technologies, and produce appropriate specifications and solutions.

LO2. Develop a coherent plan for an analytics project and present and defend the associated business case.

LO3. Design, test and implement relevant analyses using appropriate software tools including programming using an appropriate software platform.

LO4. Manage and critically appraise the success of a data analytics project using appropriate project management techniques and software.

LO5. Ensure the ethical and legal use of data collected and analysed according to established professional codes of practice.

Skills Includes intellectual skills (i.e. generic skills relating to academic study, problem solving, evaluation, research etc.) and professional/ practical skills.

LO6. Apply a range of statistical and other analytical skills appropriate to a specified business problem that may derive from an unfamiliar environment.

LO7. Specify, design, implement and test software to meet data specific project goals.

LO8. Critically assess the potential for the application of contemporary techniques not formally taught.

LO9. Function effectively as part of a project team and take responsibility for the management of functional aspects of a project.

6 Please refer to Course Development and Review Handbook or QAA website for details.


LO10. Design and maintain both technical and application documentation to a high standard.

LO11. Evaluate and assess the success of a project and make recommendations for improvement.

Apprentice’s Skills, Knowledge and Behaviours

LO12. Demonstrate the technical competencies, technical knowledge and understanding, underpinning professional, interpersonal and business skills, and behaviours that are necessary to operate as a fully competent Digital and Technology Solutions Specialist (Data Analytics).

QAA subject benchmark statement (where applicable)7

The apprenticeship standard Digital and Technology Solutions Specialist (Degree), ref ST0482, specialism occupation Data analytics specialist.

https://www.instituteforapprenticeships.org/apprenticeship-standards/digital-and-technology-solution-specialist-degree/

Mathematics, statistics and operational research benchmark and annex:

http://www.qaa.ac.uk/Publications/InformationAndGuidance/Pages/Subject-benchmark-statement-Mathematics-statistics-and-operational-research.aspx

http://www.qaa.ac.uk/Publications/InformationAndGuidance/Pages/Annex-to-Subject-benchmark-statement-Mathematics-statistics-and-operational-research.aspx

PROFESSIONAL, STATUTORY AND REGULATORY BODIES (where applicable)

Where a course is accredited by a PSRB, full details of how the course meets external requirements, and what apprentices are required to undertake, are included.

The apprenticeship is regulated by the Institute for Apprenticeships and hence must comply with the apprenticeship standard named above. No separate external accreditation is required, but the requirements set out by the standard and the respective end point assessment plan must be met. A mapping of the course modules to the KSB set out by the standard is available as a separate document. The requirements set out by the end point assessment plan are incorporated in the Learning and teaching methods, Assessment methods, the Award and classification and the Examination and assessment regulations below.

LEARNING AND TEACHING

Learning and teaching methods

This section sets out the primary learning and teaching methods, including total learning hours and any specific requirements in terms of practical/ clinical-based learning. The indicative list of learning and teaching methods includes information on the proportion of the course delivered by each method and details where a particular method relates to a particular element of the course.

Formal teaching will comprise a blend of lectures, tutorials/workshops and computer labs. Total learning hours per a 20-credit module will be 200 hours of which approximately 20% will constitute the formally taught element. It is expected that independent study, including work-based learning, will make up the remainder.

Additionally, apprentices will have an academic advisor overseeing their workplace-based activities and assessing them against the level set out in the apprenticeship standard. This will be achieved through quarterly reviews and through monitoring a log book maintained by the apprentice.

ASSESSMENT

Assessment methods

7 Please refer to the QAA website for details.









This section sets out the summative assessment methods on the course and includes details on where to find further information on the criteria used in assessing coursework. It also provides an assessment matrix which reflects the variety of modes of assessment, and the volume of assessment in the course.

Learning Outcome Assessment method Module Number of credits

LO1.

Report

Presentation

ISM122

MM701

MM702

MM706

MM709

MM720

MM721

MM722

20

10

20

20

20

60

10

30

LO2.

Report

ISM122

MM701

MM702

MM720

20

10

20

60

LO3.

Report

Presentation

MM704

MM705

MM706

MM707

MM709

MM720

MM721

MM722

20

20

20

20

20

60

10

30

LO4. Report MM701

MM720

10

60

LO5.

Practical assignment,

Report

Presentation

ISM122

MM701

MM709

20

10

20

LO6. Report

Practical assignment

MM703

MM704

MM705

MM707

MM709

MM720

MM721

MM722

10

20

20

20

20

60

10

30

LO7.

Report

Presentation

MM702

MM706

MM709

MM720

MM721

MM722

20

20

20

60

10

30

LO8. Report

MM701

MM720

10

60

LO9. Report

MM701

MM720

10

60

LO10. Report

MM701

MM706

MM720

MM721

10

20

60

10


MM722 30

LO11. Report

MM701

MM704

MM720

MM721

MM722

10

20

60

10

30

LO12. Report

Presentation MM720 60

In addition to the assessment methods above, the apprenticeship will include an independent End Point Assessment (EPA) comprising a project report and a professional discussion, which are assessed by an independent assessor. The EPA is not linked to any module and does not bear any credit value.

SUPPORT AND INFORMATION

Institutional/ University All apprentices benefit from:

Course Handbook

Extensive library facilities

Computer pool rooms

E-mail address

Welfare service

Academic advisor

Personal tutor

Course-specific Additional support, specifically where courses have non-traditional patterns of delivery (e.g. distance learning and work-based learning) include:

In addition, apprentices on this course benefit from:

Studentcentral: Virtual Learning Environment for the University of Brighton; it is a one-stop-shop for everything apprentices need while studying at the University of Brighton. Studentcentral includes pages for individual schools, modules, the ASK Study Guide and Student Life. In course and module areas apprentices will find handbooks, timetables, course announcements, course material and reading lists.

Staff: Course Leader, Module Leaders, Academic Advisors, Personal Tutors, Technical support, School Administrative support.

Specialist facilities: Specialised postgraduate computing laboratory and specialist software in multiple computing spaces throughout the School including SAS and SAS Visual Analytics, SPSS, SPSS Modeller, R, RStudio, Maple and Matlab.

Research Informed teaching

The course itself is specifically designed to nurture apprentices’ learning capabilities through research, and aims to train apprentices as critically reflective researchers and learners. The School is supported by an Industrial Advisory Board (IAB) for Mathematics and Computing and it is envisaged that a number of IAB industry partners will contribute case studies for use in the learning process.

The IAB comprises a number of Data Analytics specialists including, in particular, Tom Khabaza who is founding chairman of the Society of Data Miners, co-author of the data mining software “Clementine” and was elemental in designing the course.

All curriculum development within the Division of Mathematical Sciences is informed by pedagogical research.


More generally, within the School teaching is informed by research of very high quality. In the 2014 REF the School submitted to the Computer Science and Engineering panels. Well over 90% of submitted research was internationally recognised and around 60% was assessed to be world leading or internationally excellent.

Examples of MSc Data Analytics Course Team research include:

Dr Sonia Inacio includes in her areas of research Statistical Inference in Linear Models and in the private sector Credit Scoring, Forecasting, Logistics Regression. Example publications are: - Inácio S, Oliveira M. M., Mexia J. T. (2015) Confidence Intervals for large non-centrality parameters, Discussiones Mathematicae Probability and Statistics. - Ferreira D, Ferreira SS, Nunes C, Inácio S (2013) Inducing pivot variables and non-centrality parameters in elliptical distributions. AIP Conf. Proc. 1558: 833 (http://dx.doi.org/10.1063/1.4825625)

Dr Anestis Touloumis’s area of research is medical statistics includes in his portfolio of publications: - Touloumis, A., Tavaré, S. and Marioni, J.C. (2015). Testing the Mean Matrix in

High-Dimensional Transposable Data. Biometrics 71, pp: 157-166. - Piccirillo, S.G.M., Spiteri, I., Sottoriva, A., Touloumis, A., Ber, S., Heywood, R., Francis, N.J., Howarth, K.D., Collins, V.P., Venkitaraman, A.R., Curtis, C., Marioni, J.C., Tavaré, S., Watts, C. (2015). Contributions to Drug Resistance in Glioblastoma Derived from Malignant Cells in the Sub-ependymal Zone. Cancer Research 75, pp: 194-202.

Dr Alexey Chernov conducts research in Machine Learning. A recent publication is:

- Adamskiy, D., Koolen, W., Chernov, A., Vovk, V. A closer look at adaptive regret. Journal of Machine Learning Research, 17(23):1-21, 2016.

It is envisaged that many of the final projects will be supported and informed by the research interests of teaching staff.

Education for Sustainable Development

There is currently a burgeoning accumulation of data in almost every walk of life, ranging from internet customer behavioural data to medical, economic and financial data as well as vast stockpiles of environmental data. The combination of complex data analytics and business intelligence skills inherent in the apprenticeship provides an ideal and unique opportunity to embed and apply the principles of sustainability within the teaching and learning environment.

With the abundance of available data as noted above, there is ample opportunity for the direct exploration and analysis of large data sets within the context of global sustainability, for example environmental, climate change and health data, as well as population dynamics; an area of much interest, which can be examined in the modules “Multivariate Analysis and Statistical Modelling”, “Stochastic Methods and Forecasting”, “Medical Statistics”.

Additionally, the sustainability of business and financial environments and models as well as customer base analysis, maintenance and enhancement can be explored in the modules “Business Analytics Strategy & Practice” and “Risk Analysis and Retail Finance”.

https://staffmail.brighton.ac.uk/owa/redir.aspx?C=m3K2kjWnKUqaMbVtmZoi_a91XCjGmdMID9fTo8j7ltF7PN7TSUZtzIMWHD9-94LfNddY-0I2OFA.&URL=http%3a%2f%2fdx.doi.org%2f10.1063%2f1.4825625


PART 3: COURSE SPECIFIC REGULATIONS

COURSE STRUCTURE

This section includes an outline of the structure of the programme, including stages of study and progression points. Course Leaders may choose to include a structure diagram here.

For all apprentices, the module MM720 (Final Apprenticeship Project in Data Analytics) is mandatory.

For Route A apprentices, the taught modules are the same as for the MSc Data Analytics. The modules MM701, MM703, MM706 and ISM122 are compulsory and delivered in Semester 1. The modules MM702, MM704, MM705, MM707, MM709 are optional and delivered in Semester 2; apprentices choose three from these five optional modules. No apprentice can take both MM704 and MM707 (within either route A or route B).

For Route B apprentices, the following additional optionality is available:

the modules MM710 and MM718 from MMath Mathematics for Data Science (all year-long) may be taken as optional modules;

the compulsory modules MM703 and MM706 can be replaced either by one module MM722 or by two modules: MM721 and an extra optional module (from the Route B range of optional modules).

To progress to the Final Apprenticeship Project in Data Analytics MM720, an apprentice must pass at least 70 credits (exceptionally, the Examination Board may allow an apprentice to progress after passing at least 50 credits at the employer’s motivated request) from taught modules.

The following diagram shows the distribution of taught modules across the academic year:

Semester 1

MM703

Data Visualisation and Analysis

MM706

Programming for Analytics

ISM122

Data Management

MM701

Business Analytics Strategy & Practice

MM721

Short project in Data Analysis,

Programming and Visualisation

MM722

Extended project in Data Analysis,

Programming and Visualisation

MM710

Machine Learning and Artificial Intelligence

MM718

The Analysis of Time Series

Semester 2

MM702

Data Mining

& KDD

MM704

Risk Analysis and Retail Finance

MM705

Multivariate Analysis and

Statistical Modelling

MM707

Stochastic Methods and Forecasting

MM709

Medical

Statistics

In addition to the university modules, apprentices will be implementing their skills, knowledge and behaviour in workplace-based projects throughout their apprenticeship as agreed between the apprentice, the employer and the university and with the guidance of the apprentice’s academic advisor.

At the end of the apprenticeship, each apprentice needs to pass the End Point Assessment.

The apprentices may select different orders of taking modules depending on their background, learning needs and workplace requirements. Diagrammatic examples of two typical journeys through the apprenticeship, including the End Point Assessment, are provided on the next two pages.


Diagrammatic Example of an Apprentice’s Journey, September start, Route A

Month Job Project Taught Modules Assessment

1 October

2 November

3 December

4 January

5 February

6 March

7 April

8 May

9 June

10 July

11 August

12 September

13 October

14 November

15 December

16 January

17 February

18 March

19 April

20 May

21 June

22 July

23 August

24 September

25 October

26 November

Normal work duties / Workplace learning

In-class studies

Projects (study-work integration)

Degree assessment

Normal end of the apprenticeship

Date

Portfolio

preparation

Year

1Ye

ar 2

Year

3

Semester 1

300 hours total

In class: 1 day

(5 hours) a week

Semester 2

600 hours total

In class: 1.5 days

(9 hours) a week

Semester 3

300 hours total

In class: 1 day

(5 hours) a week

End-point assessment deadline

Exam board: degree award

A later Exam board option: degree award

Final Project

600 hours

workplace-based

Coursework

Coursework

Coursework

This diagram illustrates how teaching and other degree-related activities are distributed in a typical case. Possible variability in the calendar length of the final project and the longer route option are not shown. The indicated total study hours include self-study and are expected to be aligned with job duties to allow the apprentice to apply the newly learned skills in practice.


Diagrammatic Example of an Apprentice’s Journey, February start, Route B

Month Job Taught Modules Assessment

1 February

2 March

3 April

4 May

5 June

6 July

7 August

8 September

9 October

10 November

11 December

12 January

13 February

14 March

15 April

16 May

17 June

18 July

19 August

20 September

21 October

22 November

Normal work duties / Workplace learning

In-class studies

Projects (study-work integration)

Degree assessment

Normal end of the apprenticeship

Date

Semester 1

600 hours total

In class: 1.5 days

(9 hours) a week

Portfolio

preparation

Optionality in the

project calendar length

End-point assessment deadline

A later Exam board option: degree award

Yea

r 1

Leve

llin

g p

roje

ct

10

0 o

r 3

00

ho

urs Coursework

or Exams

Semester 2

300 hours total

In class: 1 day

(5 hours) a week

Yea

r 2

Coursework

Exam board: degree award

Final Project

600 hours

workplace-based

Projects

Semester 3

Optional

Coursework

or Exams

This diagram illustrates how teaching and other degree-related activities are distributed in a typical case. Possible variability in the dates of the end-point assessment (between February and October) is not shown. The indicated total study hours include self-study and are expected to be aligned with job duties to allow the apprentice to apply the newly learned skills in practice.


Modules

Status:

M = Mandatory (modules which must be taken and passed to be eligible for the award)

C = Compulsory (modules which must be taken to be eligible for the award)

O = Optional (optional modules)*

A = Additional (modules which must be taken to be eligible for an award accredited by a professional, statutory or regulatory body, including any non-credit bearing modules)

* Optional modules listed are indicative only and may be subject to change, depending on timetabling and staff availability

The University reserves the right to withdraw and/or replace modules.

Level8

Module code

Status Module title Credit

7 ISM122 C Data Management 20

7 MM706 C Programming for Analytics 20

7 MM703 C Data Visualisation and Analysis 10

7 MM701 C Business Analytics Strategy and Practice 10

7 MM705 O Multivariate Analysis and Statistical Modelling 20

7 MM707 O Stochastic Methods and Forecasting 20

7 MM702 O Data Mining & KDD 20

7 MM704 O Risk Analysis and Retail Finance 20

7 MM709 O Medical Statistics 20

7 MM720 M Final Apprenticeship Project in Data Analytics 60

7 MM721 O Short project in Data Analysis, Programming and Visualisation

10

7 MM722 O Extended project in Data Analysis, Programming and Visualisation

30

7 MM710 O Machine Learning and Artificial Intelligence 20

7 MM718 O The Analysis of Time Series 20

Specialist Resources:

It is anticipated that industry specialists will contribute to course delivery.

Specialist software will be provided to facilitate delivery. Examples will include SAS, R and SPSS Modeller.

Sample data sets will be created in collaboration with industrial advisors, to illustrate the application of analytics techniques on realistic large data sets.

8 All modules have learning outcomes commensurate with the FHEQ levels 0, 4, 5, 6, 7 and 8. List the level which corresponds with the learning outcomes of each module.

MSc Data Analytics

AWARD AND CLASSIFICATION

Award type Award* Title Level Eligibility for award Classification of award

Total credits9 Minimum credits10 Ratio of marks11: Class of award

Final MSc Data Analytics 7 Total credit 180 Minimum credit at level of award Other: 180

Level 7 marks Postgraduate degree

Select Post Graduate Certificate in Data Analytics

7 Total credit 60 Minimum credit at level of award 40

Level 7 marks Postgraduate Certificate

Select Post Graduate Diploma in Data Analytics

7 Total credit 120 Minimum credit at level of award 90

Level 7 marks Postgraduate Diploma

*Foundation degrees only

Progression routes from award:

Award classifications Mark/ band % Foundation degree Honours degree Postgraduate12 degree (excludes PGCE and BM BS)

70% - 100% Distinction First (1) Distinction

60% - 69.99% Merit Upper second (2:1) Merit

50% - 59.99% Pass

Lower second (2:2) Pass

40% - 49.99% Third (3)

If an apprentice fails the end point assessment, the degree cannot be awarded until the failed assessment has been passed. When the end point assessment has been passed, the MSc degree will be classified in accordance with the standard regulations.

9 Total number of credits required to be eligible for the award. 10 Minimum number of credits required, at level of award, to be eligible for the award. 11 Algorithm used to determine the classification of the final award (all marks are credit-weighted). For a Masters degree, the mark for the final element (e.g, dissertation) must be in the corresponding class of award. 12 Refers to taught provision: PG Cert, PG Dip, Masters.

MSc Data Analytics

EXAMINATION AND ASSESSMENT REGULATIONS

Please refer to the Course Approval and Review Handbook when completing this section.

The examination and assessment regulations for the course should be in accordance with the University’s General Examination and Assessment Regulations for Taught Courses (available from staffcentral or studentcentral).

Specific regulations which materially affect assessment, progression and award on the course e.g. Where referrals or repeat of modules are not permitted in line with the University’s General Examination and Assessment Regulations for Taught Courses.

Apprentices who leave the employment and hence the apprenticeship, or do not achieve sufficient credits, or fail to complete the end point assessment of the apprenticeship award, may be able to transfer to the MSc Data Analytics degree. The academic credit achieved will then be considered for an appropriate exit award.

Digital and Technology Solutions Specialist is an integrated Degree Apprenticeship and includes the End Point Assessment (EPA) which is not linked to any module and does not bear any credits. The EPA is directly and exclusively governed by the End Point Assessment Plan, Digital and Technology Solutions Specialist Integrated Degree Apprenticeship, Level 7, ST0482/AP01: https://www.instituteforapprenticeships.org/media/1966/st0482_digital-technology-solutions-specialist_l7_ap-final-for-publishing.pdf. The University’s General Examination and Assessment Regulations for Taught Courses do not apply to the EPA.

The following summarises the EPA procedures. The EPA is assessed and graded by an independent assessor from an awarding university (normally the University of Brighton) listed on the Register of End-Point Assessment Organisations, who should be independent of the course delivery. The EPA should only start once the employer is satisfied that the apprentice is consistently working at or above the level set out in the standard and the pre-requisite gateway requirements have been met and can be evidenced. The EPA must be completed over a maximum period of three months after the apprentice has met the gateway requirement. The EPA comprises a project report and a professional discussion. Each of these two assessments is graded as Fail, Pass, Merit or Distinction, and these two grades inform the overall apprenticeship grade; both assessments must be passed to pass the apprenticeship overall. One or both of failed assessments may be re-sat (within 6 months and without further learning) or re-taken; there are no limits to the number of times an apprentice may re-take / re-sit any of the assessments.

The EPA project report is based on the work in MM720, but it is different from the project report submitted within MM720. The apprentice writes the EPA project report after the university assessment for MM720 has been completed. The EPA project report presents the project work according to the requirements of the EPA plan and may significantly overlap with the MM720 project report but may also differ significantly.

The EPA professional discussion is informed by a portfolio based on workplace-based projects other than MM720. The apprentice will be guided on preparing the portfolio during the course by their academic advisor.

Exceptions required by PSRB These require the approval of the Chair of the Academic Board

https://www.instituteforapprenticeships.org/media/1966/st0482_digital-technology-solutions-specialist_l7_ap-final-for-publishing.pdf

https://www.instituteforapprenticeships.org/media/1966/st0482_digital-technology-solutions-specialist_l7_ap-final-for-publishing.pdf

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