Faculty of Science and Engineering

MSc Artificial Intelligence and Data Science

Postgraduate - Taught MSc

Open for admission in 2022/23

Open for admission in 2023/24

Start in September

Full time Part time
MSc 1 year 3 years

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.

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. Nationally, there is a widely recognised shortage of qualified Artificial Intelligence (AI) and data scientists to meet the needs of industry.

This course is will equip you with the skills and professional insight you need to launch a career in this fast-growing sector. The programme is aimed at STEM (science, technology, engineering and mathematics) and non-STEM graduates, who want to develop their digital skills. It’s also suitable for people who are looking to upskill and improve their career prospects.

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

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 or an industrial placement, where you will apply your knowledge to real-world problems, using data science and AI solutions. The University has teamed up with a range of employers to offer internship 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.

Applications for bursaries for this year are now closed. Please check back for further information on the process for applying for bursaries in 2022.

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 internships with real-world business projects.

How you’ll learn

Delivery of the core content will take place online. Face-to-face 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.

On-campus teaching will be blocked into a limited number of days per week (in 2020, this was every Friday), and fixed throughout the course. 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. You'll attend one workshop per week (maximum of six hours). 

  • Fundamentals of Data Science

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

    • Data Science Context: Datfication 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. Over six weeks, you'll attend one synchronous workshop (maximum of six hours) per fortnight.

  • 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. Over six weeks, you'll attend one synchronous workshop (maximum of six hours) per fortnight.

  • 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 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 Vizualization: Filtering, Trees, and Rules; example algorithms and using visualizations in context. 

    This module is assessed by a presentation and a project report. Over ten weeks, you'll attend one synchronous workshop (maximum of three hours) per fortnight.

  • 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. Over ten weeks, one afternoon per fortnight will be synchronous workshops (maximum of 3 hours).

  • Research and Application in AI and Data Science

    This module links the practical AI and data science techniques developed through the remaining taught modules, to their use and application in the real world. This module contains two themes that are strongly interrelated to each other. 

    • Theme 1: Case Studies.

    In this theme, you will examine a set of case studies from real world examples of the applications of both data science and artificial intelligence. The cases will be drawn from both academia and industry with attention paid not only to the spread of cases themselves, but also the diversity of the people that conducted the studies. These case studies will be used to illustrate how data science and artificial intelligence are shaping the world.

    • Theme 2: Research Proposal Development.

    In tandem with the first theme, you will develop a research proposal to tackle a genuine research project. This will prepare you for the MSc research project and dissertation in the third trimester. You will draw from the experiences in the case studies to identify questions and limitations associated with their proposed research. 

    This module is assessed by a report on the case studies, and a project proposal. Over ten weeks, one morning per fortnight will be synchronous workshops (maximum of 2 hours).

  • 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: £11,000

Full-time UK students can take out a Master’s Loan to help with tuition fees and living costs. For 2021 entry, they provide up to £11,570 for full-time Masters courses in all subject areas. Part-time UK students on the 3 year or 4 year part-time programme are not able to take out a Master’s Loan due to the length of the course.

Find out more about Postgraduate Loans.

  • EU/International: £11,000

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 2.2 undergraduate honours degree is required, or equivalent industrial experience. As the course has a strong numerical component, applicants must have studied as part of their degree programme a course or module in mathematics (pure or applied), statistics, computing, or data analysis. Equivalent professional experience to this will be considered.


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.

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.