Why study this course?
Artificial intelligence (AI) presents economists with two connected challenges. It provides new ways to construct evidence, make predictions and investigate economic questions. At the same time, it is changing how people work, how firms compete, how markets operate and how governments design policy.
The MSc Economics, AI and Data Science addresses both.
You don't need an undergraduate degree in Economics. The programme is designed for students from economics and other quantitative backgrounds who want to combine economic reasoning with AI, data science and computational methods.
You will learn to use AI, machine learning and computational methods as part of the economist’s analytical toolkit. You will also use economics to examine the consequences of AI for markets, organisations, public policy and society.
Designed and led by economists, the programme begins with economic problems rather than with technical methods in isolation. You will learn how to identify and frame a question, construct an economic model or empirical strategy, build the required dataset, select and implement suitable methods, and interpret what the results mean.
On this course, you will:
- use economic theory and data to frame and investigate real-world problems
- build and solve economic models using computational methods
- apply econometric, statistical-learning and machine-learning methods to prediction, inference and decision-making
- use Python and R to analyse data and implement economic and computational models
- construct usable datasets from complex sources, including text, images and behavioural records
- assess what AI and machine-learning methods can and cannot tell us, and interpret their outputs in an economic context
- analyse how AI is affecting markets, firms, work, productivity, competition and public policy
Economics sets the direction
Start with the economic problem, not the tool. Use economic reasoning to decide what to measure, what evidence matters, which methods fit the question and how to interpret the results.
AI in an economy workshop
Work on applied economic challenges informed by employers, policy organisations and real-world problems, explore how AI is changing markets, organisations, productivity, competition and decision-making, and develop evidence-based responses for professional and policy audiences.
Turn complex data into evidence
Use Python and R to work with data that are not ready-made for analysis. Learn how to extract, organise and analyse information from text, images, behavioural records and other complex sources.
Two dissertation pathways
Bring together economic reasoning, coding, data construction and advanced analytical methods in your own project. Choose between a Dissertation in Economics and a Dissertation in Data Science for Economists, depending on the type of question you want to investigate.
Modules
The programme is designed to take students from the foundations of economic analysis to the practical application of AI and data science.
In the first semester, students develop a strong grounding in econometrics, economic modelling and statistical learning, alongside the computational skills needed to work with modern data. In the second semester, the focus shifts towards applying these methods to economic questions, working with new forms of data and exploring how AI is transforming the economy.
A distinctive feature is the AI in the Economy Workshop, where students engage with cutting-edge research and real-world developments, discussing and critically evaluating the economic implications of AI.
The programme culminates in a choice between two dissertation pathways: a Dissertation in Economics or a Dissertation in Advanced Data Methods, allowing students to pursue their interests through either an economics-focused research project or a more data-intensive approach.
Course information may change. Module information and course content, teaching and assessment may change each year and after you have accepted your offer to study at the University of St Andrews. We display the most up-to-date information possible, but this could be from a previous academic year. For the latest module information, see the module catalogue.
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The programme follows a carefully structured pathway through economics, data science and AI.
Most modules are compulsory, ensuring a strong and coherent foundation across the degree. You will also choose one optional module and, for your final project, select between two dissertation pathways to reflect your interests and the type of research you want to pursue.
The full list of modules available for this new course is currently being finalised. Please review the ‘Why study this course’ section for the types of content that will be covered.
What it will lead to
Graduates will leave the programme with two complementary forms of judgement: the economic judgement required to identify, frame and interpret a problem, and the technical judgement required to work with models, code and complex data.
This combination is valuable because organisations need more than people who can just operate analytical tools; they also need people who can recognise the relevant economic problem, determine what evidence is required, evaluate trade-offs, identify the limits of the analysis and explain what the findings mean for decisions.
Potential career paths include:
- economist
- economic analyst
- economic data scientist
- policy analyst
- evaluation analyst
- quantitative researcher
- financial or regulatory analyst
- economic consultant
- business or market analyst
- academic researcher
- technology and AI-related strategy
Graduates may pursue careers in government departments, central banks, regulators, consultancies, financial institutions, technology firms, international organisations, research institutes and other data-intensive organisations.
Further your education
The programme also provides strong preparation for doctoral research in economics and related fields.
Through advanced econometrics, economic modelling, computational analysis and an independent dissertation, you will develop the ability to formulate and complete a substantial research project.
You can select the dissertation route that best matches your interests, whether the principal contribution is economic, empirical or computational.
Go your own way
Our Entrepreneurship Centre offers training and start-up support for those looking to freelance as well as create their own business.
It has an active entrepreneurship community, giving you access to experienced and expert mentors and an investor network, as well as one-to-one advice to help you realise your commercial potential.
Why St Andrews?
Unlike many AI and data science programmes, this degree starts with economic questions rather than technical tools. You do not need a previous degree in Economics: the programme is designed to bring economic reasoning together with AI, machine learning and computation, and to use these approaches to understand markets, work, productivity and public policy.
A coherent progression
The programme has been designed as a connected sequence rather than as a collection of separate technical modules.
In the first semester, you will build foundations in econometrics, economic modelling, computation and statistical learning.
In the second semester, you will use these foundations to construct complex datasets, apply AI methods to economic questions and investigate the wider consequences of AI for economies and societies.
This progression allows you to understand how economic theory, empirical strategy, data construction and computational analysis fit together throughout a research project.
The AI in the Economy Workshop
The AI in the Economy Workshop connects the programme’s technical training with current research and policy debates.
Through guest contributions, student-led discussion and collaborative project work, you will examine questions relating to AI, labour markets, productivity, digital platforms, financial systems, sustainability, ethics and public policy.
The workshop culminates in a group project in which you will investigate an AI-related economic issue and communicate your findings through a written report and a poster or presentation.
Applied learning and professional development
You will learn through practical classes, workshops, presentations and collaborative projects, applying your skills to real economic questions. You will also be part of a close academic community in the Business School, with access to research seminars and research expertise spanning behavioural and experimental economics, econometrics, macroeconomics, finance, public policy, data-driven research and management. You will study alongside students from a range of specialist Masters programmes and have opportunities to engage with employers, alumni and visiting researchers.
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Entry requirements
A 2.1 Honours degree from a UK university, or an equivalent international qualification, in a subject with a significant quantitative component. We welcome applicants from a wide range of quantitative academic backgrounds. Relevant subjects may include Economics, Finance, Mathematics, Statistics, Data Science, Computer Science, Engineering and Physics, as well as other disciplines where the degree has included substantial quantitative training.
Applicants should have sufficient quantitative preparation for postgraduate study, demonstrated through previous study in areas such as mathematics, statistics, econometrics, data analysis or other quantitative methods.
Prior study of Economics or experience with programming is useful but is not required.
The qualifications listed are indicative of standard requirements for entry. Some academic Schools may require applicants to achieve higher grades than the standard to be competitive. Obtaining the listed entry requirements will not guarantee you a place, as the University takes a holistic view of every application.
If your qualification does not appear in the list, it may not meet the typical requirements for entry to this programme. You may need a higher-level qualification to be competitive, for example, a Masters degree.
English language requirements
Some applicants may need to provide an English language test score to evidence their English language ability. Find out who needs to provide evidence of English language ability. Details of acceptable tests and scores are provided below.
Test scores are only normally accepted if obtained within two years of the programme start date.
Application requirements
- CV
- a detailed personal statement which includes an explanation of why you want to study the programme, why you want to enrol at St Andrews, and future career plans.
- an original signed academic reference
- academic transcripts and degree certificates
For more guidance, see supporting documents and references for postgraduate taught programmes.
Fees and funding
- UK: £15,900
- Rest of the world: £32,700
Application fee
Before we can begin processing your application, a payment of an application fee of £50 is required. In some instances, you may be eligible for an application fee waiver. Details of this, along with information on our tuition fees, can be found on the postgraduate fees and funding page.
Scholarships and funding
We are committed to supporting you through your studies, regardless of your financial circumstances. You may be eligible for scholarships, discounts or other support, such as:
Contact us
- Postgraduate online information events
- The School can help with course content, teaching and other topics: ask the School
- Ask University Admissions about how to apply, fees, scholarships and other topics
Legal notices
Admission to the University of St Andrews is governed by our Admissions policy
Information about all programmes from previous years of entry can be found in the course archive.
Curriculum development
As a research intensive institution, the University ensures that its teaching references the research interests of its staff, which may change from time to time. As a result, programmes are regularly reviewed with the aim of enhancing students' learning experience. Our approach to course revision is described online.
Tuition fees
The University will clarify compulsory fees and charges it requires any student to pay at the time of offer. The offer will also clarify conditions for any variation of fees. The University’s approach to fee setting is described online.
Page last updated: 28 October 2026