MSc Artificial Intelligence and Data Science Programme Specification

Programme Overview

The MSc Artificial Intelligence and Data Science is a work related learning postgraduate programme that integrates academic learning at masters degree level with on-the-job practical training to provide a holistic programme of education and training to meet the advanced skills needs of employers now and in the future.

This Masters Degree work-based learning programme will develop professional practice, contextualised in the workplace using industry standard technologies and approaches that are shaped by modern businesses. 

Learners may study the programme as an apprenticeship or non-apprenticeship.  The programme is identical for both streams, only differing in that the apprenticeship stream will receive an additional apprenticeship completion certificate. For both streams, the learner must be in employment, or be sponsored by an employer, with duties aligned to the advanced application of artificial intelligence (AI) and data specialisms within a business context.

The Learner will study with Northeastern University London (Provider) for approximately 49 days in the first year (one day per week for 44 weeks and one five day bootcamp) and for approximately 33 days in the second year (one day per week for 28 weeks and one five day bootcamp). 

Additionally, the learner and employer will commit to a further two days per week, for provider-guided work-based training. The MSc award is 180 credits and learners will be considered part-time learners by Northeastern University London. Each course, typically 15 credits, is assessed by a range of activities aligned to industry norms, i.e. almost all assessments relate to workplace activities that are expected in an AI and data related occupation. The content, and consequently the learning outcomes and methods of assessment, vary between courses. Where possible, assessments will be undertaken in the workplace.

The programme begins with ‘Programming for Data Science’ that focuses on programming principles and practice for data science applications. It is fast-paced, accelerating the learner from fundamental programming principles to applied data science tasks. 

This is followed by ‘Data Driven Analytics’ that systematically explores the modern data science workflow with an emphasis on scalable, high-performance architectures for big data storage and analytics. 

Next, the programme considers ‘Advanced Statistical and Mathematical Methods’ laying the mathematical foundations for the proceeding technical courses. 

This is followed by ‘Stakeholders, Negotiation and Project Management’ that develops the tools and methodologies required to be an effective change agent within a modern business before putting theory into practice in ‘Applied Machine Intelligence’, in which learners investigate how to use AI and machine learning methodologies. 

The programme then considers ‘Communication in Business’, exploring a range of communication methods used in business and the digital sector, including technology roadmaps, business reports, digital solution documentation and management-level presentations. 

Learners then explore ‘Data Engineering’ where they examine engineering principles to investigate and manage the design, development and deployment of new data products within business. 

This is followed by ‘The Social Context of AI and Data Science’, where learners gain a critical understanding of how these technologies present ethical questions that require a deep understanding both of the detailed context of application, and of pertinent philosophical questions. 

The taught programme concludes with an ‘AI Capstone Project’, which is conceived and executed by the learner in their workplace. This will demonstrate a professional-level of technical and analytical skill, aligned to achieving organisational goals and enabling effective institutional change. 

Dedicated Apprenticeship Advisors will undertake regular workplace visits (approximately every six/eight weeks) and provide supplementary support.

Full Time Programme Structure

Michealmas Term

Hilary Term

Trinity Term

Structure of the MSc Artificial Intelligence and Data Science Programme

Optional ‘work ready bootcamp’ (0 credits)

Compulsory Courses (Level 7)

NCHNAP782 Programming for Data Science (15 credits)

NCHNAP783 Data Driven Analytics (30 credits)

NCHNAP784 Advanced Statistical and Mathematical Methods (15 credits)

NCHNAP785 Applied Machine Intelligence (30 credits)

NCHNAP786 Stakeholders, Negotiation and Project Management (15 credits)

NCHNAP787 Communication in Business (15 credits)

NCHNAP788 Data Engineering (15 credits)

NCHNAP789 The Social Context of AI and Data Science (15 credits)

Project background and design bootcamp (0 credits)

NCHNAP790 AI Capstone Project (30 credits)

Entrance Requirements

The learner will need to be in employment with, or sponsored by, a hiring business, with responsibilities to be aligned to the Level 7 Artificial Intelligence and Data Science Apprenticeship.

Entry requirements are agreed then set, based on numerous factors including availability of additional on-the-job support, by both the Employer/Sponsor and Provider. As such, entrance requirements may vary. Learners are selected based on their application and an assessment process, which is tailored to the learner’s work-based learning position.

Typically, employers require:

• A first class, 2:1 or 2:2 degree level award in a STEM subject
• At least Grade 4/C GCSE Maths, English and IT

Some applicants may not have traditional qualifications as listed above, and have prior learning and skills developed from the workplace, these will be considered on a case-by- case basis.

Learners will also need to meet the government’s eligibility criteria:

• Have been a UK/EU/ESS resident for the past three years or more prior to starting the programme.

● Have left full-time education prior to the start date of the apprenticeship.

● Be aged at least 16 years old to meet government funding criteria.

Recognition of Prior Learning

For the apprenticeship stream, where a learner is eligible to apply for the recognition of prior learning on the basis of certificated or experiential learning, this will be considered in the Initial Needs Analysis, as per Education Skills and Funding Agency (ESFA) Funding Rules, and will take due consideration of the Northeastern University London Recognition of Prior Learning and Credit Transfer Policy.

Aims of the Programme

The overall aim of the programme is to:

● Offer specialist postgraduate study that underpins the Level 7, Artificial Intelligence (AI) Data Specialist apprenticeship.

● Offer a programme of study that meets the needs and expectations of businesses and organisations and supports the career development of AI and data science professionals.

● Provides flexible access to a structured learning experience that is designed to support the acquisition of advanced skills within a range of organisations and businesses.

● Support the development of specialist AI and data science skills that will be valued and supported within professional work-based contexts.

● Place the advanced study of AI and data science within a specialist framework that provides learners with specific, contextual and transferable knowledge and skills.

● Develop an advanced understanding of the key principles, theories and technologies that support the professional practice of AI and data science.

● Provide learners with a rich academic experience that encourages the integration of theory and practice within the workplace.

● Instil a strong professional approach that promotes independence of thought, empathy and a critical awareness of ethical, legal and social issues related to AI and data science.

● Encourage and support a raft of advanced attributes including, independence, project management and critical self-reflection.

● Blend the development of advanced mathematical, computing and technical understanding with a raft of related transferrable skills that enable learners to develop their careers and operate successfully as AI and data science specialists within a range of professional contexts.

Programme Learning Outcomes

Knowledge and Understanding

A student will be able to: 

Subject Specific Skills

A student will be able to: 

Transferable and Professional Skills

A student will be able to: 

All of the above learning outcomes are mapped to the relevant QAA Level 7, FHEQ and Apprenticeship Standard

Map of Courses to Learning Outcomes

Teaching and Learning Strategies

strategies

The work-based learning programme is studied through blended work-based learning, over a 21 month period, and is delivered through the online interactive virtual learning environment (VLE).

The achievement of the Programme Learning Outcomes is supported primarily through an extensive range of e-learning interactions and materials. Delivery methods include:

• Lectures (synchronous face-to-face or via over the web technology, and asynchronous pre-recorded)
• Seminars for small group discussion (including online discussion)
• Informal discussion groups (including online discussion)
• Assessments
• Links to related reading material
• Individual learning plans (ILP)
• Online presentations/animations
• Participation in online forums
• Independent study and research
• Final project

Regular in-depth formative feedback is provided to the learner, with advice and guidance to support their achievement in summative assessments. The programme aims to progressively enhance AI and data science knowledge and skills – as well as maths, English and communication skills – as they practice and apply their knowledge and skills in the workplace. Regular tri-partite reviews between the learner, their Apprenticeship Advisor (provider) and workplace line manager (employer/sponsor) formally monitor and evaluate the learner’s progress.

The blended-learning work-based programme ensures that learners have the opportunity to explore their subject in a structured, well-managed and appropriate manner. It develops the knowledge, core and subject-specific skills, and transferable skills, required by learners and enhances their confidence to apply these within a professional environment. The combination of academic study and work-based learning is a key feature of the apprenticeship. Practical and theoretical experiences in the workplace, in tandem with their academic studies, develop and enhance the learner’s specialist knowledge, skills and behaviours.

Assessment tasks mix written assignments with set exercises, computer-based examination and technical tasks to provide opportunity for learners to demonstrate their learning in a variety of ways and to support professional practices within the workplace.

Learners are also supported to acquire and practice a wide range of transferable skills. These include problem solving, analysis, strategic thinking and interpersonal and communication skills. Learners will be effective collaborators within their work environments and fully participate in the presentation of work during their studies. Importantly, they are also encouraged to balance these cooperative interpersonal skills with advanced levels of responsibility and self-development within the apprenticeship. These graduate qualities are supported throughout the apprenticeship from an initial rigorous non-credit one-week block of classroom teaching (bootcamp), that includes the core computing and business skills needed to become an effective team member in the workplace, as well as an introduction to the demands and challenges of the apprenticeship, advanced study skills and needs/expectations of employers.

Induction for all new learners includes a welcome to Northeastern University London by the Assistant Vice President for Digital Innovation & Enterprise Learning; introduction to key personnel including Student Support and Development (SSD); the Programme Leader; Course Leaders; and Apprenticeship Advisers to introduce learners to the programme they are about to embark upon. There are also sessions on library services, IT and facilities, and an induction from the Quality Team.

The programme is designed to progress steadily over 21 months and develop learners’ conceptual sophistication through cumulative experience and knowledge. The final project will allow learners to develop their thinking in collaboration with an academic supervisor.

Northeastern University London recognises and has embedded the expectations of current equality legislation, by ensuring that the programme is as accessible as possible by design.

Additional alternative arrangements for learners with Inclusive Learning Plans (IncLPs)/declared disabilities will be made on an individual basis, in consultation with the relevant policies and support services.

Applicants with a disability are encouraged to declare their disability during the application process under the Initial Needs Analysis. Once declared, SSD will work with the learner to agree a support plan for the duration required. This plan will form part of the Commitment Statement and will be reviewed at the tri-partite reviews every six/eight weeks to confirm that this support is effective.

SSD facilitates all academic and learner services and oversees learner wellbeing; careers advice is provided for learners via Northeastern University London Careers Team.

Assessment

Course are assessed in a variety of ways including:

Formative

• Tests or quizzes
• Essays or reports
• Short answers and problem sets
• Oral presentations

Summative

• Computer-based examination
• Written assignment
• Report
• Dissertation
• Practical skills assessment
• Oral assessment
• Presentation
• Set exercise

Appendix C contains the programme structure and assessment summary.

Assessment Regulations

The assessment regulations can be found on the Northeastern University London website.

Awards

This programme is studied over 21 months as a blended, work-based, learning programme, whereby the learner will study with the provider for approximately 49 days in the first year (one day per week for 44 weeks and one five-day bootcamp) and for approximately 33 days in the second year (one day per week for 28 weeks and one five-day bootcamp). This is termed ‘off-the-job’ training. Additionally, the learner and employer will commit to a further two days per week, for 44 weeks in the first year and 28 weeks in the second year, for provider-guided work-based tasks and training. The 21-month programme is worth 180 credits (= 1800 hours of learning time), comprising multiple courses. This allows for six weeks of annual leave in the first year and four weeks of annual leave in the second year (over the remaining nine months).

The final 30 credits (= 300 hours of learning time) will comprise the workplace project, spanning 12 weeks.

Learners must complete the required amount of off the job training hours which equates to 6 hours per week on average for those who work at least 30 hours per week. The total hours required is calculated by the number of practical training weeks of the course x 6 hrs. Learners must complete the minimum required hours to complete the apprenticeship.

Learners must successfully complete each course in order to be awarded the specified number of credits for that course. One credit corresponds to approximately ten hours of ‘learning time’ (including all online and face-to-face delivery, all private study and research, and relevant aspects of on-the-job learning). Thus obtaining 180 credits requires 1800 hours of overall learning time.

All courses are compulsory and must be successfully taken by all learners studying the programme and learners must attend face-to-face courses/bootcamps.

To be eligible for the award of a MSc Artificial Intelligence and Data Science learners must obtain 180 credits, all at level 7.

Learners will achieve 105 credits in calendar year 1 and 75 credits in calendar year 2, totalling 180 credits.

Learners who achieve 60 credits at level 7 will be eligible for the award of a Postgraduate Certificate Artificial Intelligence and Data Science. 

Learners who achieve 120 credits at level 7 will be eligible for a Postgraduate Diploma Artificial Intelligence and Data Science.

Where a learner fails a course(s) due to illness or other mitigating circumstances, such failure may not be compensated or condoned.

The grading of the MSc degree award is made up of the AI Capstone Project assessment together with numerous course assessments. 

Classifications

Learners are graded using postgraduate degree classifications for English universities, and following the QAA (Quality Assurance Agency for Higher Education) Code of Practice for the Assurance of Academic Quality and Standards in Higher Education. The Northeastern University London postgraduate award grading regulations are shown below:

Exemptions from the Northeastern University London Academic Quality Framework

None

Special Provisions for Professional Statutory and Regulatory Body

None

Quality Evaluation and Enhancement

Review and Evaluation Mechanisms

Northeastern University London has robust procedures, as described in AQF4 Programme and Course Approval and Modifications and AQF5 Annual Monitoring and Reporting, in place to assure the quality of the programme’s development, delivery, and management, alongside the systematic monitoring, ongoing review and enhancement of all programmes awarded by Northeastern University London. Enhancements are made as necessary to ensure that systems remain effective and rigorous.

Northeastern University London utilises constructive feedback from a variety of sources, internal and external, to inform its decision-making process to enhance the programme and the learner experience. These feedback sources include:

• Annual programme reports, written by the Programme Director, are prepared in order to enhance individual programmes and to plan ahead.

• Annual Examiner reports are prepared by independent External Examiners to confirm that a programme has been assessed in accordance with the approved documentation and that the learner performance meets the appropriate academic standards.

• Education and Skills Funding Agency Employer and Apprentice surveys.

• Formal learner feedback mechanisms consist of course and programme learner satisfaction questionnaires and Apprentice Voice Committee.

• Informal learner feedback is also valued by Northeastern University London and this can take the form of learners talking to their Apprenticeship Advisor (which incorporates the personal tutor role), Lecturers, professional staff, or elected learner representative.

In addition to academic progress monitoring, progression also includes checking that the learner is achieving planned levels of off-the-job learning required by the apprenticeship as set out in the Commitment Statement. This six/eight-weekly discussion between the Apprenticeship Advisor, line manager and learner will also confirm whether the learner is keeping pace with their plan of learning at work, and is meeting the competency progression points as part of their apprenticeship.

Learner attendance at scheduled learning opportunities, as well as monitoring periods of off- the-job training, is monitored through the use of an online Learner Management System.

About this Document

Disclaimer 

The University has checked the information provided in this Programme Specification and will aim to deliver this programme in keeping with this Programme Specification. However, changes to the programme may sometimes be required arising from annual monitoring, student feedback, and the review and update of courses and programmes. Where this activity leads to significant changes to courses and programmes there will be prior consultation with students and others, wherever possible, and the University will take all reasonable steps to minimise disruption to students. It is also possible that the University may not be able to offer a course or programme for reasons outside of its control, for example, due to the absence of a member of staff or low student registration numbers. Where this is the case, the University will aim to inform applicants and students as soon as possible, and where appropriate, will facilitate the transfer of affected students to another suitable programme. 

Copyright 

The contents of this Programme Specification are the copyright of the University and all rights are reserved. No part of this publication may be reproduced, stored in a retrieval system or transmitted, in any form or by any means, such as electronic, mechanical, photocopied, recorded or otherwise, without the prior consent of the University.

Appendix A – Map to QAA Level 7 – Masters Degree Descriptor

QAA benchmark statements can be found here.

Appendix B – Map to Apprenticeship Standard

Appendix C – Programme Structure and Summative Assessment Summary

COURSE TYPE:   C = Compulsory; O = Option.

COURSE MODE:   CD = Campus Delivery; BK = Block Delivery; BL = Blended Learning; DL = Distance Learning and Self-Directed Learning; EL = E-Learning; EX = Experiential; PL = Placement; WB = Work Based Learning,

ASSESSMENT WEIGHTING: AE1 = Assessment Element 1; AE2 = Assessment Element 2; AE3 = Assessment Element 3