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Data Science and Artificial Intelligence (Conversion Programme) MSc

Prepare for the jobs of the near future with this conversion MSc programme, designed to enable learners without computer science backgrounds to train as artificial intelligence data specialists.

This programme is ideal for highly motivated applicants seeking to develop knowledge and technical expertise in data science and AI. Since applicants come from various academic backgrounds, we strongly recommend that you review our guidance for completing your application tailored specifically to this programme.

  • Discover data science and AI principles to create solutions for business and industry challenges.
  • Learn from experts engaged in cutting-edge research in Machine Learning, Natural Language Processing, Computer Vision, and Game AI.
  • Receive intensive teaching on programming and statistics, to equip you for the specialised modules in this programme.
  • This programme was developed by a collaborative partnership of universities and industry partners, brought together by the Institute of Coding.

Study options

Starting in
September 2025
Location
Mile End
Fees
Home: £12,850
Overseas: £31,500
EU/EEA/Swiss students

What you'll study

Please note that this course will commence earlier than other Queen Mary courses. This early start is mandatory to provide you with two weeks of intensive pre-sessional teaching, equipping you with the essential technical skills required to successfully complete the course.

This degree programme aims to help learners without strong backgrounds in programming or statistics to acquire a level of skill in these areas, before learning to apply them to specialist AI techniques.

During the programme, you’ll discover and devise data-driven AI solutions that can be applied to support and enhance organisational decision-making. You’ll also explore the essential ethical and legal issues that need to be considered when developing AI technology.

You’ll attend campus for two weeks of intensive teaching at the beginning of the programme. These two intensive preparatory modules will allow you to acquire the pre-requisite skillset in programming and statistics, before diving into more specialised modules.

Before starting the programme, you can complete a free online short course, Get Ready for a Masters in Data Science and AI, developed by a collaborative partnership of leading higher education institutions, including Queen Mary. The short course is designed to help you identify whether you’re ready for masters study, improve your data science skills, and get to grips with the basics of Python.

Structure

  • Two preparatory modules.
  • Six compulsory modules.
  • Compulsory final year project.
Masters Open Evening

Masters Open Evening

Join us at Mile End campus for our next postgraduate open event on Wednesday 5 February 2025 and see why Queen Mary is the perfect place to take your academic or professional journey to the next level.

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Compulsory/Core modules

This module provides a comprehensive overview of the challenges of risk assessment, prediction and decision-making covering public health and medicine, the law, government strategy, transport safety and consumer protection. Students will learn how to see through much of the confusion spoken about risk in public discourse, and will be provided with methods and tools for improved risk assessment that can be directly applied for personal, group, and strategic decision-making. The module also directly addresses the limitations of big data and machine learning for solving decision and risk problems.

Principles of Machine Learning covers the fundamental concepts, methodology and practical tools necessary to understand, build and assess data-driven models to describe real-world systems and predict their behaviour. We will follow the standard machine learning taxonomy to organise problems and techniques into well-defined families (supervised and unsupervised learning) and subfamilies. We will pay particular attention to the methodology that we need to use to avoid and identify common pitfalls. State-of-the-art models and the latest developments on model deployment will be discussed.

This module provides an intensive practical introduction to programming in Python, suitable for students with some degree of mathematical or statistical maturity. It covers a range of practical skills and underlying knowledge. These include the basic programming constructs for control, data structuring and modularisation; the use of systems for collaborative development and version control such as Git; unit testing and documentation; project structures and continuous integration/deployment.

This module has two components. The first introduces students to the use of probability and statistics in the context of data analysis. The module starts with basics of descriptive statistics and probability distributions. Then we go on with applied statistics techniques, such as visualisation, fitting probability distributions, time-series analysis, and hypothesis testing, which are all fundamental to the exploration, insight extraction, and modelling activities that are fundamental in handling data, of any size. The second covers some basic matrix algebra, including matrix multiplication and diagonalisation.

This module takes a practical approach to the coverage of ethics in Artificial Intelligence and Data Science. It sees ethical considerations as part of a spectrum of concerns, including ethics, but extending through regulation and legal compliance as formal expressions of what is and is not ethical. It considers examples of the kinds of issues that arise in existing systems, and uses the UK Government's Ethical Framework as an example of how to embed considerations of ethics into business processes.

The module covers the theoretical underpinnings and practical applications of Neural Networks and automatic differentiation as a tool for modern AI. Neural Networks & Deep Learning are now the method of choice for solving various Machine Learning problems. They are applied to several real-world problems not only within Academia but most importantly within Industry. Knowledge of Neural Networks and how to apply them to solve practical problems is now considered one of the most essential skills in the job market for a CS graduate. The module will include a detailed exposition for Neural Networks and their implementation using a Deep Learning framework. Topics covered include but not limited to: Automatic Differentiation, Stochastic Gradient Descent, Regression, Softmax Regression, Multi-Layer Perceptrons, Training of Neural Networks and hyper-parameter optimization, Convolutional Neural Networks, Recurrent Neural Networks. Applications of Neural Networks to Vision and NLP.

The field of information retrieval (IR) aims to provide techniques and tools to support effective and efficient access to large amounts of textual information (e.g. stored on the web, digital libraries, intranets). This involves representation, retrieval, presentation and user issues. The following topics will be covered: 1. Application of representation and retrieval approaches described in the Foundations of Information Retrieval module, Semester A, in the context of structured documents, in particular web documents, and digital libraries. 2. Databases & information retrieval, and logical models for information retrieval. 3. The organisation of documents according to categories (e.g. Yahoo directory) or their content to provide more effective presentation of the collection to the users. 4. The design of interfaces and visualisation tools that aim at supporting end-users in their search tasks. 5. User aspects, including the evaluation of IR systems according to user satisfaction, and the incorporation of user information seeking behaviour in the search task. The module consists of 3 hours per week of lectures for 12 weeks, including labs and tutorials.

The MSc project gives you an opportunity to apply the techniques and technologies that you have learnt to a significant advanced project. Projects will either be significantly development based or have a research focus that will require you to undertake practical work. All projects will be expected either to investigate or to make use of techniques that are at the leading edge.

Data that has relevance for decision-making is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the Internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and electronic patient records. Data mining is a rapidly growing field that is concerned with developing techniques to assist decision-makers to make intelligent use of these repositories. The field of data mining has evolved from the disciplines of statistics and artificial intelligence. This module will combine practical exploration of data mining techniques with a exploration of algorithms, including their limitations. Students taking this module should have an elementary understanding of probability concepts and some experience of programming.

Please note that all modules are subject to change.

Assessment

The intensive preparatory modules are assessed by submission of a completed lab book and a short in-class diagnostic test.

Standard modules are assessed through a combination of coursework and written examinations.

The project is assessed via a written report, a formal oral presentation and, where applicable, a demonstration of any software and/or hardware you have developed.

Research project

Individual projects are undertaken during the summer months, under the supervision of an academic member of staff, with whom there are weekly consultancy meetings. These are used for students to report on their progress, discuss research design, and plan their future work.

The Projects Coordinator also runs taught sessions to support the project module. A number of industrial-linked projects are offered each year, which students can apply for.

Teaching

The two preparatory intensive modules will be taught through lectures and iterative learning approaches over two weeks, taking place before the start of the academic term.  

Teaching for all other modules includes a combination of lectures, seminars and use of a virtual learning environment. Each module provides contact time with your lecturers, supported by lab work and self-directed further study.

You will be assigned an Academic Advisor who will guide you in both academic and pastoral matters throughout your time at Queen Mary.

Where you'll learn

Facilities

The School has excellent bespoke facilities, including:

  • Augmented human interaction (AHI) laboratory with 350 state-of-the-art computers.
  • Antenna measurement laboratory.
  • Media and arts technology studios (performance lab, control room, listening/recording room).
  • Robotics laboratory (ARQspace).

Campus

Teaching is based at Queen Mary’s main Mile End campus, one of the largest self-contained residential campuses in the capital. Our location in the heart of London’s East End offers a rich cultural environment.

We have invested £105m in new facilities over the past five years to offer our students an exceptional learning environment. Recent developments include the £39m Graduate Centre, providing 7,700 square metres of learning and teaching space.

The campus is 15 minutes by tube from Central London, where you will have access to many of the University of London’s other facilities, including the Senate House library.

The Graduate Centre on the Mile End campus.
The Graduate Centre on the Mile End campus.

About the School

School of Electronic Engineering and Computer Science

The School of Electronic Engineering and Computer Science carries out world-class research – and applies it to real-world problems. Being taught by someone who is changing the world with their ideas makes for exciting lectures, and helps you to stay ahead of the curve in your field. 99 per cent of our research is classed as ‘world-leading’ or ‘internationally excellent’ (REF 2021).

We are proud of our excellent student-staff relations, and our diverse student body, made up of learners from more than 60 countries.

The School has a close-knit student community, who take part in competitions and extracurricular lab activities.

Career paths

Data and AI-driven technologies are becoming integral parts of our lives, helping us to solve everyday problems as well as drive scientific and technological progress.

This year, Sky News reported that demand for jobs requiring skills in AI and machine learning is estimated to soar by 40% over the next five years.

This programme aims to teach you the principles used to investigate and manage the design, development and deployment of new data products within organisations, preparing you for employment as a data science specialist in a wide range of industries.

Fees and funding

Full-time study

September 2025 | 1 year

Conditional deposit

Home: £2000

Overseas: £2000
Information about deposits

Queen Mary alumni can get a £1000, 10% or 20% discount on their fees depending on the programme of study. Find out more about the Alumni Loyalty Award

Funding

There are a number of ways you can fund your postgraduate degree.

Our Advice and Counselling service offers specialist support on financial issues, which you can access as soon as you apply for a place at Queen Mary. Before you apply, you can access our funding guides and advice on managing your money:

Entry requirements

UK

Degree requirements

A 2:1 or above at undergraduate level in a Science, Technology, Engineering or Mathematics (STEM) subject not related to Computer Science, Electrical Engineering or Electronic Engineering.

Other routes

Applicants with a 2:1 in any other subject can also be considered provided the degree contains satisfactory study in programming or statistics.

Applicants with degrees obtained more than five years ago who do not meet the above criteria, and those with relevant experience will be considered on an individual basis and must demonstrate evidence of:

  • Aptitude and technical skills normally acquired through employment OR
  • Evidence of successful completion of relevant programming qualifications, such as those provided by the Institute of Coding

Where possible, selection will be based on the written application, but applicants may be asked to attend an interview to support the evaluation of their experience and aptitude.

Find out more about how to apply for our postgraduate taught courses.

International

English language requirements

The English language requirements for our programmes are indicated by English bands, and therefore the specific test and score acceptable is based on the band assigned to the academic department within which your chosen course of study is administered. Note that for some academic departments there are programmes with non-standard English language requirements.

The English Language requirements for entry to postgraduate taught and research programmes in the School of Electronic Engineering and Computer Science falls within the following English band:

Band 4: IELTS (Academic) minimum score 6.5 overall with 6.0 in each of Writing, Listening, Reading and Speaking

We accept a range of English tests and qualifications categorised in our English bands for you to demonstrate your level of English Language proficiency. See all accepted English tests that we deem equivalent to these IELTS scores.

Visas and immigration

Find out how to apply for a student visa.

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