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Academic Handbook MSc Artificial Intelligence and Computer Science

MSc in Artificial Intelligence and Computer Science Programme Specification

Programme Title and Award MSc in Artificial Intelligence and Computer Science
Programme Level Level 7 HECoS Code 100359 (AI) 50% 

100366 (CS) 50%

Relevant QAA Benchmark Statements Computing (Master’s) Programme Code LMSAICS-F


Awarding Body Northeastern University – London Language of Instruction English
Teaching institution Northeastern University  London Date approved July 2023
Mode of study Full Time

Part Time

Duration of Study 1 Year (FT)

2 Years (PT)

HESA Cost Code Programming for Data Applications – 121 (8.33%)

Fundamentals of Computation, Data, and Algorithms – 121 (8.33%)

Theory and Applications of Data Analytics – 121 (8.33%)

AI and Data Ethics – 141 (8.33%)

Web Services – 121 (11.11%)

Programming Design Paradigm – 121 (11.11%)

Database Management Systems – 121 (11.11%)

MSc Dissertation Project – 121 (33.33%)

Programme Summary

This programme is designed to prepare students for a career in computing science and artificial intelligence, with a particular focus on software engineering: the design, analysis, development, and maintenance of software on emerging platforms (e.g. Web) and application domains (e.g. data analytics, machine learning). The programme is suitable for students who want to step into a career in software development.

Students will have the opportunity to: (i) master the fundamentals of programme design, best programming practices (e.g. for testing), algorithms and data structures; (ii) learn how to program front-end applications, via specialised courses on Web and mobile application development, as well as back-end applications, via specialised courses on databases and scalable distributed systems; and (iii) master the fundamentals of machine learning algorithms, a crucial component of many modern applications that students can learn to integrate into their own. Finally, (iv) students will have the opportunity to concentrate their aforementioned learning outcomes to an individual software development project, producing an original piece of software on an application domain of their choice to enhance their portfolio.

Programme Integration

The programme comprises of 120 credits spread over seven taught courses as well as a 60-credit individual software development project. The seven taught courses provide a solid foundation of the tools, methods and techniques that any graduate software developer needs to master and that are applicable in many career settings. The Artificial Intelligence dissertation project (60 credits) is a sustained piece of independent work on an agreed topic of the student’s choice, in line with the programme focus.

Before students embark on the study of the MSc programme, they are able to engage in an introductory, non-credit bearing course (i.e., a bootcamp or taster course) on programming and mathematics fundamentals. The Introduction to Programming course is accessible to all MSc students pursuing computer science and technology-related programmes at Level 7. Students are strongly advised to avail themselves of this course, as it serves as a crucial foundation for acquiring essential programming and mathematical skills. Introduction to Programming encompasses a comprehensive understanding of software development principles, problem-solving techniques, data representations, control flow, probability, calculus, and the construction of Python programs. Some students may find it necessary to enhance their mathematical or programming proficiencies prior to embarking on their MSc programme, while others may require a refresher to align their knowledge and understanding with the teaching standards at Northeastern University – London. The course is available online through our Virtual Learning Environment prior to starting the main MSc programme.

The creation, implementation, and effects of Artificial Intelligence (AI) systems all have an ethical dimension. As AI develops and integrates into more facets of our lives, it is crucial to make sure that its application adheres to ethical standards and considerations. These considerations will be addressed throughout the programme by using seminar discussions, forums, literature and teacher guidance on the implications of AI implementation and adoption. It will also be specifically addressed in the core AI ethics course. Students on this programme, will be able to understand and communicate the accountability, transparency and explainability of AI, as well as will be able to comprehend how AI systems make decisions and their impact. In addition, by the end of the programme, students will be able to understand ethical AI use and take into account potential social, economic, and environmental implications with the goal of advancing society as a whole rather than just a chosen few.

The programme is designed and delivered so as to integrate component parts into a whole that ensures students graduate with both the breadth of modern software engineering skills and knowledge, and the depth of delivering specialised software artefacts.

Full Time Programme Structure

Semester One

LDSCI7234 Programming for Data Applications (15 credits)

LCSCI7235 Fundamentals of Computation, Data, and Algorithms (15 credits)

LDSCI7236 Theory and Applications of Data Analytics (15 credits)

NCHAI749 AI & Data Ethics (15 credits)

Semester Two

LCSCI7225 Programming Design Paradigm (30 credits)

LCSCI7224 Web Services (15 credits)

LCSCI7228 Database Management Systems (15 credits)

Semester Three

LDSCI7237 Artificial Intelligence Dissertation Project (60 credits)

Part Time Programme Structure

Year One

Semester One

LDSCI7234 Programming for Data Applications (15 credits)

LCSCI7235 Fundamentals of Computation, Data, and Algorithms (15 credits)

Semester Two

LCSCI7228 Database Management Systems (20 credits)

LCSCI7224 Web Services (15 credits)

Semester Three

Begin LDSCI7237 Artificial Intelligence Dissertation Project (60 credits)

Year Two

Semester One

LDSCI7236 Theory and Applications of Data Analytics (15 credits)

NCHAI749 AI & Data Ethics (15 credits)

Semester Two

LCSCI7225 Programming Design Paradigm(30 credits)

Semester Three

Complete LDSCI7237 Artificial Intelligence Dissertation Project (60 credits)

Entrance Requirements

Entry requirements – Our typical offer for postgraduate study is an upper second-class honours undergraduate degree (or the equivalent) in computing or in a related discipline with substantial computing or mathematical content; but each applicant will be assessed on an individual basis, including relevant professional experience where applicable. If English is not an applicant’s native language, they will need to demonstrate proficiency in English in order to study at the University. For a list of equivalencies, please check here.

Recognition of Prior Learning

Where a student wishes to apply for the recognition of prior learning on the basis of certificated or experiential learning, they should follow the University’s Recognition of Prior Learning and Credit Transfer Policy.

Aims of the Programme

The programme aims to:

  • Enable students to confidently apply key software engineering principles, algorithms and tools to solve computing problems of varying complexity, showing originality in the application of their knowledge.
  • Develop students’ skills of working in small teams and individually in software projects on emerging platforms and application domains demonstrating decision-making skills, using their initiative and taking responsibility.

The overall aim of the programme is to:

  • Produce graduates who are proficient in the design, implementation and testing of software, utilising their independent learning skills to continue to advance their knowledge and skills in software engineering.

Learning Outcomes

Knowledge and Understanding

A student will be able to: 

K1d Master the practical methodology of software development, knowing emerging trends, tools and technologies and how they can be applied in modern industrial applications.
K2d Demonstrate a critical awareness of advanced aspects of the theory and practice of software engineering, and consistently produce correct, well-structured programs, guided by appropriate software engineering design principles and best programming practices (from theory to practice).
K3d Evaluate technical, management and societal dimensions of computer software  and demonstrate a comprehensive understanding and critical awareness of key issues (from technical to societal) surrounding computer software.
K4d Critically review and analyse key developments in a particular problem area, identify limitations, and propose directions for further innovation.

Subject Specific Skills

A student will be able to: 

S1d Critically assess software problems and recognise the individual tools, libraries and techniques suitable for solving the problem and develop their interactions (including any missing component) to produce a software solution.
S2d Critically evaluate the requirements and limitations of tools in various stages of the software development life cycle.
S3d Design and develop original software of varying levels of complexity in a variety of programming languages, making appropriate decisions given incomplete or missing data.
S4d Become a sophisticated software developer, familiar with codes of ethics, codes of practice, and relevant industrial standards; including data management and use, security, equality, diversity, and inclusion (EDI), and sustainability.

Transferable and Professional Skills

A student will be able to: 

T1d Lead and/or participate in team projects: envision a technically sound solution to a computing problem, share it with peers, encourage them to participate, and deliver it in a timely manner according to specification.
T2d Consistently display an excellent level of technical proficiency in written English and command of scholarly terminology, so as to be able to deal with complex issues in a sophisticated and systematic way.
T3d Learn effectively and independently new topics and tools related to the courses of the programme.
T4d Communicate effectively to both technical and non-technical audiences through oral presentations, software demonstrations, and written reports.

All of the above learning outcomes are mapped to the relevant QAA Subject Benchmark threshold statements – see Appendix A. For the exit awards see Appendix B.

Map of Courses to Programme Learning Outcomes

Course Title Knowledge and Understanding Subject-Specific Skills Transferable and Professional Skills
  K1d K2d K3d K4d S1d S2d S3d S4d T1d T2d T3d T4d
Programming for Data Applications X X X X X X X X X X X X
Fundamentals of Computation, Data, and Algorithms X X X X X X X X X X X X
Theory and Applications of Data Analytics X X X X X X X X X X X X
AI & Data Ethics X   X X X   X X   X X X
Programming Design Paradigm X X X X X X X X X X X X
Database Management Systems X X X X X X X X X X X X
Web Services X X X X X X X X X X X X
Artificial Intelligence Dissertation Project X X X X X X X X X X X X

Teaching and Learning Strategies

Teaching Methods

  • Seminars, including some with student presentations and lab sessions
  • Small classes
  • Individual coding-based tutorials
  • Feedback on coding assignments and technical reports
  • Student presentations
  • Online discussion forums
  • Individual project supervisions (which support both written and oral communication skills)
  • (Structured) independent study and research

The University teaches in small groups and is committed to providing individual attention and guidance. Lectures and seminars include student interaction and dialogue. As indicated below, students have an opportunity to participate in the Faculty’s research seminars and audit other lectures and seminars of their choice. Assessment, as indicated above, is in a variety of modes: exam, coursework essay, coding assignment, conference-style oral presentation with PowerPoint or handout, and a software engineering project with presentation.

Learning Opportunities

The Faculty’s regular research seminars offer a lively and varied menu of talks and discussions involving both internal and invited speakers. MSc Computer Science (Software Development) students are invited and encouraged to attend these in all three terms.

Students will also be encouraged to attend the broad programme of liberal-arts professorial lectures at the University.

Inclusive Teaching and Learning

The University is deeply committed to widening participation, both through outreach activities and through a teaching environment that is inclusive towards a variety of backgrounds and learning styles. Faculty are engaged in the public dissemination of their discipline, visiting a wide range of schools, hosting open lectures, engaging with the media, and publishing in accessible formats. Faculty strives to support a lively, open, and interactive teaching environment, in which research and teaching are mutually complementary.

The high staff-student ratio at the University is especially important to the faculty’s ability to give individualised attention to students, and thus to be inclusive towards a variety of backgrounds and learning styles. The faculty facilitates a wide range of academic and social events in which academics and students are brought together.
The University will make reasonable adjustment for students with disabilities, in accordance with the recommendations of Student Support and Development (SSD). Where necessary, following consultation with SSD, alternative forms of assessment may be offered.

The variety of modes of assessment in this programme may render it more inclusive than those which assess in more uniform ways.


The University ensures students are supported outside of class contact time by means of a virtual learning environment (VLE), through which students access learning materials and communicate with fellow students and faculty.


Assessment Method

  • Examination
  • Project
  • Presentation

Appendix C is the programme structure and assessment summary.

Assessment Regulations

The University’s Assessment Regulations for Taught Awards can be found here.

Student Support

Disabilities and/or Specific Learning Difficulties (SPLDS)

Students are strongly encouraged to inform the University of any medical conditions, disabilities, specific learning difficulties (SpLD) or neurological differences as soon as is practical. Students will be asked to submit supporting documentation from a doctor, clinical or educational psychologist detailing the nature of their disability and the impact it is likely to have on their studies in order to help us put in place appropriate support and accommodations. More information can be found in the Student Disability Policy here. This data is managed and securely stored by SSD. During Freshers’ Week, a number of talks and events are held which are designed to support and inform students with regard to mental health, disabilities, safety and learning support.

SSD meet with students as soon as possible, and preferably before the start of the academic year, to discuss their needs and draft a Learning Support Plan (LSP) which outlines the support to be provided both within the University (if appropriate) and externally. If requested by the student, the SDD will then arrange to inform relevant faculty of the student’s needs and any reasonable adjustments required.

If a student is undiagnosed but believes they may have a SpLDS (e.g. Dyslexia) the SDD will help them to access diagnostic services. If the assessment confirms a SpLDS, the SDD will work the student in preparing a LSP and will provide advice about accessing additional funding and support through the Disabled Students Allowance, where a student may be eligible.

For more information, please click here.

Employability Skills

  • Programming skills: deliver original, technically sound software solutions to computing problems using appropriate software development methods and techniques that adhere to the code of practice and industry standards of the particular problem area
  • Leadership skills: engage in a peer review process to critically assess proposed solutions, providing constructive feedback on project design, management and evaluation
  • Communication skills: communicate solutions via presentations, demonstrations or technical reports to both technical and non-technical audiences

Careers Education, Information and Guidance

Masters students will have access to the University’s Careers Advisory Service. This includes employer receptions with representatives from a wide range of sectors and our electronic Careers Centre, containing features and functionality for careers guidance, interview advice and job searching.
In addition, Careers Advisers, supplemented with support from tutors, offer advice, often one-to-one, on securing a professional future tailored to students’ skills and ambitions.

Quality Evaluation and Enhancement

Award Standards

Every programme of study is developed by the Faculties, utilising their subject specialists and approved by the University’s Academic Board.

Review and Evaluation Mechanisms

The University 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 development, delivery, management, systematic monitoring and ongoing review and enhancement of all University programmes. Enhancements are made as necessary to ensure that systems remain effective and rigorous.

The University utilises constructive feedback from a variety of sources, internal and external, to inform its decision-making process to enhance the programme and student experiences. These feedback sources are listed below:

  • Annual Course Reviews, written by the Course Leader, are prepared to enable the Course Leader to reflect on the course, using a variety of data and student/faculty feedback to enhance the course and support the Programme Director in writing the Annual Programme Review
  • 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, as appointed by the University, to confirm that a programme has been assessed in accordance with the approved documentation and that the student performance meets the appropriate academic standards
  • Formal student feedback mechanisms consist of student representatives attending meetings; course satisfaction surveys; and annual programme satisfaction surveys
  • Informal student feedback is also valued by the University and this can take the form of students talking or corresponding with their faculty or professional staff.

Version History

Title: MSc Artificial Intelligence & Computer Science Programme Specification

Approved by: Academic Board

Location: Academic Handbook/programme specifications and handbooks/postgraduate programme specifications/

Version number Date approved Date published Owner Proposed next review date Modification (As per AQF4) & category number
1.3 January 2024 January 2024 Dr Alexandros Koliousis June 2028 Category 1: Corrections/
clarifications to documents which do not change approved content or learning outcomes
1.2 November 2023 November 2023 Dr Alexandros Koliousis June 2028 Category 1: Corrections/
clarifications to documents which do not change approved content or learning outcomes
1.1 July 2023 July 2023 Dr Alexandros Koliousis June 2028 Category 1: Corrections/
clarifications to documents which do not change approved content or learning outcomes


June 2023 Dr Alexandros Koliousis June 2028
Referenced documents Recognition of Prior Learning and Credit Transfer Policy; Assessment Regulations for Taught Awards; Student Disclosure Form; AQF4 Programme and Course Approval and Modifications; and AQF5 Annual Monitoring and Reporting.
External Reference Point(s) QAA Subject Benchmark Statement Computing (Master’s)


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. 


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 Subject Benchmark Computing (Master’s)

  Recommendation* Learning Outcomes
5.1 The study of computing at master’s degree level is typically characterised by:
  an ability to evaluate the technical, societal and management dimensions of computer systems K3, S4
  a knowledge and understanding of advanced aspects of computer systems and their use K1, K2, S1
  a combination of theory and practice, with practice being guided by theoretical considerations K2, K4, S1, S2, S3, S4
  a strong emphasis on the underlying discipline and/or applications S3, S4
  the mastery of the practical methodology of the relevant area of computing, whether for general application in software development or in specialised applications relating to the storing, processing and communication of information K1
  an understanding of professional, legal, social, cultural and ethical issues related to computing and an awareness of societal and environmental impact. K3, S4
5.2 Master’s degree courses in computing/IT should seek to include the development of the following subject-specific skills:
  an ability to engage in a peer review process that involves the critical review of papers, software and proposals, coupled with positive advice for improvement and innovation S1, S2, T1
  competences at a systems level appropriate to the learning outcomes of the course: the ability to assess systems (which may include software, devices, people, and so on), to recognise the individual components and to understand their interaction, to improve systems, to replace them and to create them S1, S2, S3
  familiarity with codes of ethics and codes of practice specific to the specialism of the degree course, relevant industrial standards and principles underpinning the development of high integrity systems (for safety, security, trust, privacy, and so on), while keeping in focus the benefits of, approaches to and opportunities offered by innovation K3, S4
  translational skills which involve the necessary communication between technical and non-technical audiences T2, T4
5.3 Master’s degree courses in computing/IT should seek to include development of the following generic skills:
  those required for the creation of the lifelong learner, who can set goals and identify resources for the purpose of learning T3, S4
  an ability to critically review the literature, which includes identifying all of the key developments in a particular area of study, critically analysing them and identifying limitations and avenues for further development or explanation K3, K4, S1, S2, S3, T3
  an ability to recognise and respond to opportunities for innovation K4, T3, S3
  leadership skills, which tend to be characterised by acquiring a vision (based on sound technical insights) coupled with the ability to encourage others to share in that vision and to ensure that this will not be to their detriment. T1


Appendix B – Exit Awards

Postgraduate Certificate

60 credits

Postgraduate Diploma

120 credits

Appendix C – Programme Structure and Assessment Summary

Code Course Title Credit Type Mode Assessment Weighting % & Activity Type (code overleaf)
AE1 Activity
AE2 Activity
AE3 Activity
FHEQ Level 7
LDSCI7234 Programming for Data Applications 15 C CD 50% A 50% A
LCSCI7235 Fundamentals of Computation, Data, and Algorithms 15 C CD 40% Set 60% Exam
LDSCI7236 Theory and Applications of Data Analytics 15 C CD 50% A 50% A
NCHAI749 AI and Data Ethics 15 C CD 50% Set 50% P
LCSCI7224 Web Services 15 C CD 50% Set 50% P
LCSCI7225 Programming Design Paradigm 30 C CD 50% A 50% A
LCSCI7228 Database Management Systems 15 C CD 40% P 60% Exam
LDSCI7237 Artificial Intelligence Dissertation Project 60 C CD 20% A 60% Diss 20% Oral


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; AE4 = Assessment Element 4

Written exam Exam
Take home exam TEx
Written assignment A
Report R
Dissertation Diss
Portfolio F
Project output (other than dissertation) P
Oral assessment and presentation Oral
Practical skills assessment Pract
Set exercise Set