MSc in Artificial Intelligence and Data Analytics Programme Specification

Programme Summary

The MSc Artificial Intelligence and Data Analytics will enable students to specialise in artificial intelligence and data analytics to work in an economy where this skill set is greatly needed. Students will build on their previous computational expertise and acquire the skills they need to become artificial intelligence and data engineers with commercial awareness.

By studying this programme, students will gain an in-depth understanding of the statistical and mathematical foundations of artificial intelligence and achieve advanced practical knowledge of artificial intelligence and machine learning methodologies applied to complex datasets to meet business objectives. Students will learn the engineering principles used to investigate and manage the design, development and deployment of new data products within a business. Students will also gain the knowledge and ability to apply the techniques applicable to data engineering, data pipelines and the design, development and deployment of scalable data solutions. Furthermore, they will gain the knowledge and skills to manage successful artificial intelligence and data analytics projects through effective communication and stakeholder management skills. The comprehensive skill set will equip students with the ability to analyse data and gain insights, as well as improve problem-solving skills, critical thinking, and analytical abilities.

Programme Intergration

The programme comprises six 15-credit taught courses and one 30-credit taught courses, as well as a 60-credit MSc dissertation project.

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.

In their first semester, three 15-credit computing courses teach students the theory and application of data analytics. Students learn the basics of programming (e.g., if-then-else statements, for loops and data collections), alongside the fundamentals of computing (e.g., logic operators, algorithm complexity and data structures, respectively). They also learn how to ingest and transform data (e.g., numerical arrays, images, or text), how to design and structure programs, and how to design and structure programs. Additionally, one 15-credit course teaches students ethical considerations while handling data.

In their second semester, one 30-credit and one 15-credit courses teach students the fundamental concepts and techniques used in modern machine learning applications through code and visualisations, as well as the principles and concepts of data engineering and big data architecture. Additionally, one 15-credit management-oriented course teaches students how to use the principles, practices and methodologies used in project management.

The 60-credit individual MSc dissertation project is a sustained piece of independent work on an agreed topic of the student’s choice, in line with the programme focus. It runs throughout the year so that students have ample time to focus on their independent learning with the right guidance from their supervisor(s).

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 the above component parts into a whole that ensures students graduate with both a technical and theoretical understanding of artificial intelligence and data analytics and their applications, as well as a broad, contextual appreciation of its implications.

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)

LPHIL7252 AI & Data Ethics (15 credits)

Semester Two

LDSCI7227 Machine Learning (15 credits)

LDSCI7229 Advanced Data Engineering (30 credits)

LCSCI7233 Project Management and Communication (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)

LDSCI7236 Theory and Applications of Data Analytics (15 credits)

Semester Two

LDSCI7229 Advanced Data Engineering (30 credits)

Semester Three

Begin LDSCI7237 Artificial Intelligence Dissertation Project (60 credits)

Year Two

Semester One

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

LPHIL7252 AI & Data Ethics (15 credits)     

Semester Two

LCSCI7233 Project Management and Communication (15 credits)

LDSCI7227 Machine Learning (15 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 science or related discipline; 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

• Produce graduates who are proficient in the lifecycle of data analytics and artificial intelligence to create business value, utilising the appropriate programming languages and algorithms to deliver robust artificial intelligence and data dependent solutions.
• Enable students to develop an advanced understanding of the key principles, theories and technologies that support the professional practice of artificial intelligence and data analytics with awareness of relevant legal, ethical, professional and regulatory constraints.
• Blend the development of advanced mathematical, computing and technical understanding with a raft of related transferable skills that enable students to develop their careers and operate successfully as artificial intelligence and data analytics specialists within a range of professional contexts.

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 Subject Benchmark threshold statements – see Appendix A. For the exit awards see Appendix B.

Map of Courses to Programme Learning Outcomes

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 regular research seminars offer a lively and varied menu of talks and discussions involving both internal and invited speakers. Students are invited and encouraged to attend these.

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 adjustments 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.

E-Learning

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

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

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 Subject Benchmark Computing (Master’s)

Appendix B – Exit Awards

Postgraduate Certificate

4 x 15 credit Level 7 courses = 60 credits

Postgraduate Diploma

8 x 15 credit level 7 courses = 120 credits

Appendix C – Programme Structure and 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; AE4 = Assessment Element 4