Find out more about studying MSc Artificial Intelligence and Data Science at University of Hull? We've gathered all the key details, entry requirements, modules, fees, and more. Take the next step by booking an open day to explore it for yourself.
Master of Science - MSc
London Campus
Full Time
Sep 2027
1 Year
Data is one of today’s most valuable commodities. AI touches almost every walk of life – from improving diagnosis and treatment in healthcare to new creative tools for artists.The need for skilled graduates who understand the full breadth of the technology is greater now than ever before.Want to change direction? Switch careers? Upskill? This fast-track Masters is for you.Taught in our £4.5 million Centre of Excellence for Data Science, Artificial Intelligence and Modelling (DAIM), our MSc offers a fast track to career success in this dynamic field.As a conversion course, this MSc is suitable for students with a range of backgrounds in STEM and non-STEM subjects. With intensive teaching, your in-person lectures and workshops will normally be one day a week, 9 am to 6 pm, so you can balance your studies alongside other commitments.Don’t have programming experience? That’s okay. You’ll learn Python coding at the start of the course to make sure you’re up to speed.Unlike other universities, you’ll study the full breadth of AI, not just one specialism. You’ll cover programming, statistics, machine learning, big data, data visualisation, computer vision and the ethical and legal responsibilities of using data.You can design your own research project to suit your background and career interests. Some students may be able to with work on a research project with one of our industry partners such as Naimuri, the NHS, KCOM or Lampada Digital Solutions.You’ll develop key skills including programming, problem-solving, and data visualisation and interpretation. And graduate at the forefront of data science.
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.
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 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.
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.
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.
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.
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.
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