Master of Data Science with AI Application

Entry requirements

We require a 2:1 Bachelor (Honours) degree or international equivalent in any degree subject.

Applicants with a strong data science or mathematical background may wish to consider our MSc Scientific Computing and Data Analysis programmes.

Months of entry

September

Course content

The Master of Data Science with AI Applications is designed to equip aspiring professionals with the latest tools and techniques used in the sector empowering you to manage, analyse, and extract insights from complex data, and preparing you for high-impact roles in AI-driven fields such as tech, healthcare, finance, and beyond.

The Durham MDS with AI Applications provides training in contemporary data science in a supportive environment, learning from practicing researchers who are making a difference across a range of industries.

From personalised medicine, to smart cities and sustainable solutions, data science is building a better world. At the same time, developments in AI technologies have made the field of data science more accessible than ever, creating new opportunities to gain insight into the interactions between people, AI systems and their environment. This has led to a significant increase in demand for skilled data scientists with specialisation in AI.

The MDS programme opens up a future in data driven careers even if your first degree is in an unrelated subject (including the social sciences, the arts and humanities). The programme equips you with the skills to process and analyse data, communicate your findings to a wide audience whilst applying this knowledge to practical situations.

The programme culminates with a research project, an in-depth investigation in which you apply the skills learned during the course to a research problem working alongside an expert in the area.

The course is part of a suite of courses which share some common modules, you can choose the general route or take one of the specialist pathways in:

• Bioinformatics and Biological Modelling
• Digital Humanities
• Environmental Data Science
• Health
• Social Analytics
• Heritage

You’ll begin by mastering a range of introductory modules before progressing to more advanced contemporary techniques in machine learning to expand your knowledge and understanding. We offer an extensive range of optional modules which allows you to focus on an area of interest such as text analytics and data visualization.

Course structure

Year 1 modules

Core modules:

The Data Science Research Project

is a substantial piece of self directed research into an unfamiliar area of data science, or in your subject specialisation area with a focus on data science. The project can be practical, theoretical or both, and is designed to develop your research, analysis and report-writing skills.

Critical Perspectives in Data Science and AI

develops your understanding of the production, analysis and use of quantified data, and how to analyse these practices anthropologically utilising AI applications. You will learn to think ethically and contextually about quantified data, and how to apply this knowledge to practical problems in data science, including your own research project.

Programming for Data Science

uses the popular Python software packages used in a wide range of industry settings. You will learn how to gather, manipulate and process real-world data and learn the key concepts of data analysis and data visualisation.

Ethics of Artificial Intelligence and Data Science

introduces contemporary debates on ethical issues and bias resulting from the application of data analytics, statistical modelling and artificial intelligence in society. You will learn about contemporary philosophical research on these issues and how to apply this research in practice. The module includes an essay about an ethical topic, completed under the guidance of a tutor.

Machine Learning

introduces the essential knowledge and skills required in machine learning for data science using the R statistical language. You will develop an understanding of the theory, computation and application of topics such as modern regression methods, decision-based machine-learning techniques, support vector machines, and neural networks.

AI and HCI Interactions

explore how humans and intelligent systems interact in the real world. This module introduces the theories, methods, and tools for designing and evaluating interactive AI systems through a human-centred lens, examining how design choices influence user experience, trust, and ethical outcomes. You'll gain the skills to create AI technologies that are not only intelligent but also intuitive, responsible, and socially aware.

Optional modules:

The remainder of the course will be made up of core and option modules which will vary depending on prior qualifications and experience. These have previously included:

• Introduction to Mathematics for Data Science
• Introduction to Computing for Data ScienceText Mining and Language Analytics
• Multilevel Modelling
• Data Exploration, Visualisation and Unsupervised Learning
• AI Recommender Systems

Information for international students

International students who do not meet direct entry requirements for this degree might have the option to complete an International Foundation Year.

Fees and funding

More information is available here: Tuition fees - how much are they - Durham University