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DAIM student Kuniko Azuma with lecturer

Faculty of Science and Engineering

MSc Artificial Intelligence and Data Science

Postgraduate - Taught MSc

Open for admission in 2023/24

Start in September, January

Qualification Full time Part time
MSc 1 year 2 years

About the course

Data is one of the 21st century’s most valuable commodities. Understanding how to analyse, validate and interpret it to inform decision making are key skills in just about every walk of life.

Whether you want to enhance your existing knowledge or change career, this master’s-level conversion course offers a fast track to career success by teaching you how to extract value from data in domains of interest to you.

The power of Artificial Intelligence (AI) and data science can be seen in almost every walk of life and at every level of society, from improving diagnosis and treatment in healthcare, to offering new creative tools for artists. Nationally, there is a recognised shortage of qualified data practitioners to meet the growing needs of employers, and this demand extends across business sectors.

Our students come from a broad range of academic and professional backgrounds, including English Literature, Maths, Music, Politics, Psychology, Physic, Marketing, Biology, Computer Science and more. Through lectures, hands-on workshops and a self-led research project, you will widen your existing skills and knowledge with current data science and AI techniques to help you launch a career in these rapidly-growing fields.

You will learn Python coding, so experience in programming is not required.

Plus, you'll be based in our £4.5 million Data Science, Artificial Intelligence and Modelling (DAIM) building, which houses the largest computational teaching space on campus.

Using these brand new facilities, you’ll cover topics such as programming, statistics, machine learning, big data, data visualisation, computer vision and the ethical and legal responsibilities of using data. Learning is delivered online and on-campus through a series of bespoke taught modules. In the third semester, you will do an academic dissertation, which can be a project linked to industry, where you will apply your knowledge to real-world problems, using data science and AI solutions. We have teamed up with a range of employers who may be able to offer project opportunities to students.

At the end of the course, graduates will have developed key competencies in AI and data science, including programming, data visualisation, problem-solving and data interpretation.

You will be able to apply AI and data science techniques to real-world problems; critically evaluate AI and data science methodologies; plan, design and carry out empirical research, and interpret, present and communicate the outcomes of data science and AI solutions.

The course combines expertise from Departments across the Faculty of Science and Engineering, including Computer Science, Physics and Mathematics. You’ll have access to VIPER – one of the most powerful high-performance computers in the Higher Education sector. Our Computer Science research is ranked 5th in the UK for impact.

Artificial Intelligence and Data Science Bursaries

To support widening participation students undertaking full-time study in 2023/24, we are pleased to announce a one-off round of scholarships. Applications for these bursaries will open to prospective new students who have applied to join the course in September 2023 and have paid their fee deposit.

Applications for this round of scholarships will be launched in late April 2023 once we have received information from the UK Government.

The scholarships will be prioritised for women, black, disabled or low-income background students, and are being funded by the Office for Students (OfS), Department for Digital, Culture, Media and Sport (DCMS), Department for Business, Energy and Industrial Strategy (BEIS) and the Office for Artificial Intelligence (OAI). Find out more.

What you'll study

Key areas of study for the programme will include concepts and methodologies related to AI, data science and data analytics. The programme will begin with an intensive module in programming skills where you will learn to code from scratch, to ensure everyone is prepared to undertake the data science and AI modules that follow.

  • Core programming skills and techniques, including designing and coding applications, and the important principles of code design and development.
  • Data science tools and techniques, including the principles of data science, data analysis, visualisation and interpretation, and the use of “big data”.
  • Artificial intelligence tools and techniques, including problem-solving, knowledge representation, machine learning, computer vision, human-computer interactions and (mis) information diffusion.
  • Ethical computing and data science, exploring the ethical, legal, social and professional frameworks in which data scientists must operate, in business and society.
  • The application of AI and data science in research and industry

You will take one AI and one data science module in trimester 1, together with the programming module.

In trimester 2, further AI and data science modules will allow you to develop your skills and knowledge and prepare for your dissertation by exploring case studies and developing a research proposal.

The dissertation project will be based either with one of our industry partners, solving real-world data science or artificial intelligence problems, or in a disciplinary area relevant to your background and/or career goals. 

The University is working with a range of regional partners, including Humber Outreach Partnership, Spencer Group, J.R Rix & Sons, KCOM, The Deep, Lampada Digital Solutions, Optalysys and C4DI to offer real-world business project opportunities.

How you’ll learn

Sessions will focus on problem-solving, group work and the application of core knowledge, and allow the use of the University’s computer science facilities.

You'll learn in our brand new, £4.5 million Data Science, Artificial Intelligence and Modelling (DAIM) building, which houses the largest computational teaching space on campus. Spread over two levels, the building has hundreds of seats for students to learn, practice, and apply their coding technique to unique problems that face the world related to data science, artificial intelligence, and modelling.

Lectures and teaching take place in-person on the University campus, usually on one day per week. You will need access to a computer to complete course work outside of these sessions. High specification computational facilities are available to students on-campus. You will be encouraged to bring your professional and learning experiences to your degree, and to work with students of differing experiences to maximise the benefits of the programme.

There will be online support to help with the transition to postgraduate study, incorporating topics such as returning to study, expectations, the language of postgraduate study, and time management.

Learning, teaching and assessment will be characterised by diverse assessment types and an avoidance of recall-based timed examinations. You will be allocated a tutor to support your academic development.

All modules are subject to availability and this list may change at any time.

  • Programming for AI and Data Science

    The module content is designed to ensure that you are equipped with the fundamental programming skills in Python to allow you to successfully undertake the remainder of the programme.

    Techniques that will be taught include:

    • Installation and Packages, including specific packages for machine learning, data science, and artificial intelligence.

    • Fundamentals of coding

    • Documentation, File Handling and Lists, Variables and Data Types, Functions and Loops, Conditions and Data Manipulation, Graphing and basic visualization, Debugging and testing.

    This module is assessed by a portfolio of work.


  • Fundamentals of Data Science

    An introduction to the principles of data science and data analysis. Topics include:

    • Data Science Context: Datafication of society and the history of data science; Properties and types of data (e.g., quantitative and categorical data)

    • Classification and regression, introduction to Kaggle and other sources of data

    • Data Management: Data collection and techniques; Cleaning of data and processing; Data errors and artefacts; missing data

    • Introductory statistical approaches to data: Introduction to probabilities; Descriptive statistics (e.g., centrality measures) and characterizing distributions; Correlations; Statistical hypothesis testing

    • Data analysis and visualization: Types of visualization and interpretation; Regression models

    • Applications: Real-world data applications, including examples

    • The legal framework and ethical implications of data science

    This module is assessed by a presentation and project report.


  • Understanding AI

    An introduction to the fundamental concepts in Artificial Intelligence, and their application. Topics include:

    • Origins of AI: What is AI?; From early history to the Dartmouth conference and the present day; Intelligent agents, and performance measures.

    • Learning, Frameworks and Packages: Introduction to supervised and unsupervised learning; Convolution neural networks, Capsule neural networks; Keras; Tensorflow

    • Implications for Society: Legalities; Ethics and professional implications; Social consequences

    This module is assessed by a portfolio of work.


  • Big Data and Data Mining

    This module builds on the concepts introduced in the first data science module and provides an introduction to Big Data and Data Mining, including distance measures. Topics will include:

    • Databases, including the use of the SQL language.

    • Association Pattern Data Mining: the frequentist approach; Apriori algorithm.

    • Clustering: filtering; k-means and associated approaches.

    • Outliers: definitions of outliers; Extreme values and their detection

    •  Data Classification and Visualization: Filtering, Trees, and Rules; example algorithms and using visualizations in context. 

    This module is assessed by a presentation and a project report.


  • Applied Artificial Intelligence

    The module will build on the concepts introduced in the first Artificial Intelligence module and provide you with the skills and knowledge to undertake your dissertation. Topics will include:

    • Classification: revisiting the classification problem; Hyperplanes; Naive Bayes classifiers

    • Deep Learning: neural networks; Autoencoders and Deep Learning

    • Applications to problems: real-world problems, and solving them with AI, Natural language processing

    • Legal, societal, ethical and professional implications of using AI, including cognitive bias in AI solutions and the implications for equality.

    This module is assessed by a presentation and a project report.


  • Research and Application in Artificial Intelligence and Data Science

    This module offers options in how Artificial Intelligence and Data Science are applied to
    real-world contexts. By the end of the module, you will have formulated your own plans and literature reviews for your final MSc project. The module contains two themes that are strongly inter-related to each other.

    Theme 1 – Options

    You will be offered a selection of tailored studies in Artificial Intelligence. These options will comprise:

    Option 1 – Sustainability
    Option 2 – AI in Healthcare
    Option 3 – Social Responsibility
    Option 4 – The Creative Industries
    Option 5 – The Natural Environment

    Each of these options represents a specialist application of Artificial Intelligence and Data Science into a particular area. This will enable you to select and investigate applied aspects of AI and Data Science taught by specialists in this area and to consider projects in these areas, which is related to Theme 2.

    Please note that not all of these options will necessarily run in every year and will be dependent on demand.

    Theme 2: Research Proposal Development

    In tandem with the first theme, you will develop with guidance your own research proposal to tackle a genuine research project. This will prepare you for the MSc research project and dissertation in the final trimester. You will draw from the experiences in the options to identify questions and limitations associated with your proposed research.


  • Artificial Intelligence and Data Science Research Project

    This module is the capstone of the MSc, drawing together the skills and knowledge built up through the programme and allowing you to demonstrate your competence in using AI and/or data science techniques.

    This module aims to develop your ability to:

    • Plan and work independently on a complex research-based problem in artificial intelligence and/or data science.

    • Report on the aims, methods and outcomes of a scientific investigation.

    Each research project will be particular to the individual concerned. The actual work carried out will depend on the topic but may include experimental planning and organisation of equipment and/or services (e.g., VIPER), computer programming in artificial intelligence and/or data science, data acquisition and analysis, study of related literature and critical evaluation of the outputs.


Where you'll study

The location below may not be the exact location of all modules on your timetable. The buildings you'll be taught in can vary each year and depend on the modules you study.

Click to view on Google Maps
Hull Campus

Click to view directions on Google Maps

Fees and funding

  • Home £11,550

UK students can take out a Masters Loan to help with tuition fees and living costs. For 2023 entry, they provide up to £12,167 for full-time and part-time taught and research Masters courses in all subject areas. Find out more about Postgraduate Loans.

  • International£14,000/year

International applicants may need to pay a tuition fee deposit before the start of the course. Visit our tuition fee deposit page for more information.

Please see the terms and conditions for International fees 2022/23

Graduate PGT Scholarship

The University of Hull is pleased to offer graduates progressing from undergraduate to postgraduate taught study a £1,000 scholarship towards the cost of their tuition fees.

Find out if you’re eligible by visiting the University of Hull Graduate PGT Scholarship page.

Scholarships and Bursaries

The University offers a range of scholarships to help you with your studies.

For more information, please visit the Scholarships and Bursaries page.

Want to change direction? This programme is suitable for students from STEM and non-STEM degrees who want to develop their data skills.

You’ll have access to VIPER – one of the most powerful high-performance computers in the Higher Education sector.

Our Computer Science research was ranked 5th in the UK for impact in the latest national assessment of research.

Entry requirements

As a conversion MSc, this programme has been specifically designed to be accessible to students with a wide variety of non-computational backgrounds.

A minimum 2.1 undergraduate honours degree is required. As the course has a strong numerical component, applicants must have studied a course or module in mathematics (pure or applied), statistics, computing, or data analysis as part of their degree programme.

A personal statement outlining these competencies is required from all applicants to evaluate their suitability for the course.

A more detailed personal statement outlining their eligibility for the scholarship will be required for students applying for scholarship funding.

In order to ensure our students have a rich learning and student experience, most of our programmes have a mix of domestic and international students. We reserve the right to close applications early to either group, if application volumes suggest that this blend cannot be achieved. In addition, existing undergraduate students at the University of Hull have a guaranteed ‘Fast Track’ route to any postgraduate programme, subject to meeting the entry criteria (excluding Social Work).

International students

If you require a student visa to study or if your first language is not English you will be required to provide acceptable evidence of your English language proficiency level.

This course requires academic IELTS 6.5 overall, with no less than 6.0 in each skill. See other English language proficiency qualifications accepted by this University.

If your English currently does not reach the University's required standard for this programme, you may be interested in one of our English language courses.

Visit your country page to find out more about our entry requirements.

All applicants to supply a personal statement to display evidence of Mathematics, statistics and/or programming competencies, and their practical applications either in a professional or academic setting. This is required to evaluate the applicants suitability for the course.


Future prospects

The integration of our business partners in the design and delivery of the programme will place a key focus on employability for graduates of the programme. Employability, and the evidencing of developing digital skills, will be embedded throughout the programme, with additional support from the Careers, Entrepreneurship and Study Abroad service at the University. The course will prepare you to work as a data scientist in a wide range of industries, or progress to further study in a broad range of disciplines.