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Economics and Data Science MSc

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Find out more about our Economics and Data Science Master's degree at Warwick

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A group of economic students at the Universirty of Warwick

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P-L1I1

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MSc

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1 year full-time

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22 September 2025

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Economics

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University of Warwick

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This highly quantitative one-year MSc course equips you with the skills to analyse and interpret complex economic phenomena in today’s data-driven, dynamic global economy. The availability of big data and the use of algorithms in decision-making, whether in the private or public sector, has made it essential to have the ability to parse through large datasets, provide data-driven solutions and critically analyse their results.

The programme offers intensive training in state-of-the-art data science methods as well as economics and econometrics. This unique combination provides students with the tools to navigate the data economy through hands-on experience with analytical tools, machine learning techniques, and computational methods. You will gain expertise in critical analysis, data-driven problem-solving, and tackling technical challenges.

The MSc offers a combination of strong core studies, specialist options and the opportunity to conduct academic research under the guidance of world-leading experts. Warwick's Department of Economics, ranked 1st in the UK (The Good University Guide 2025) and 23rd in the World (The QS World University Subject Rankings 2024), provides professional training in modern economics.

Graduates will be well-prepared to meet the rigorous standards of top national and international institutions and to excel in high-level PhD programs, to join leading public policy research institutions, or enter the private sectors where such skills are highly in-demand.

Applications for the MSc Economics and Data Science are open now. For specific questions about this course, please get in touch with us: economics.pgoffice@warwick.ac.uk

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Our MSc in Economics is particularly well-suited for students in Economics who wish to specialise or gain additional skills in Data Science and Machine Learning and how such skills may be applied within Economics, Public Policy, Industry and related sectors. The course links economic theory and empirical economics with the fast-developing field of Data Science. You will acquire knowledge and skills in statistical (machine) learning, and use these in your study of applications in macroeconomic and microeconomic theory, econometrics and big data.  An 8,000-word dissertation provides an opportunity to explore your own interests across these fields of study. 

You will be automatically enrolled on a pre-sessional Mathematics and Statistics programme to equip you with the relevant methodological skills you need to succeed. 

By the end of the course, you will be experienced in working with large amounts of data to generate new findings and insights relevant to problems in economics. You will also be proficient in standard coding languages, like R and Python, to work on those problems.

Skills from this degree

  • Rigorous advanced training in economic analysis and techniques, which includes opportunities to contribute to current economic research and debates
  • Data science techniques and methods pertaining to applications in Economics
  • Training in the acquisition, management and processing of large volumes of data with applications in Economics
  • Analytical approach to thinking about national and international economic problems, policies and decision-making
  • Research skills; use of library and internet as information sources; locating, extracting, analysing, and presenting material
  • Numeracy and quantitative skills; use of mathematics and diagrams, understanding data, statistical analysis
  • IT skills; coding; word processing and spreadsheets; specialist econometric or statistical software; internet applications
  • Written and oral communication skills 

Frequently Asked Questions

View a full list of frequently asked questions from the Department of Economics.Link opens in a new window

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You will have a combination of lectures, and small group support and feedback classes. You will also learn through independent study, which will include reading journals and books, completing problem sets and assessments, revising for exams and undertaking research.

In the summer term and summer vacation your independent study time will increase as you complete your research dissertation. Your dissertation work will normally be individually supervised on a one-to-one basis and we have an effective personal tutor system providing individual support.

We encourage one-to-one interaction with our world-leading academics and offer great flexibility in the optional modules that allow you to specialise or diversify your studies.

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Your lecture size will naturally vary, especially for the optional modules, but also for core lectures.

Some of the larger modules may have 50-200 students in them. You will then typically have weekly support and feedback classes with around 15-20 students. 

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An average of 8-10 hours of lectures and 3 hours of classes per week.

Your timetable

Your personalised timetable will be complete when you are registered for all modules, compulsory and optional, and you have been allocated to your lectures, seminars and other small group classes. Your compulsory modules will be registered for you and you will be able to choose your optional modules when you join us.

Pre-Sessional Mathematics and Statistics

The MSc Economics is a quantitative degree and students will need to be competent in several areas of mathematics. You are required to attend a pre-sessional course, Introductory Mathematics and Statistics, which will be taught in the two weeks before the University's main term begins.

The course is designed to ensure that your maths and statistical knowledge and skills are at the standard required for you to succeed on the rest of the MSc course and it consists of both lectures and a small learning group. Further information can be found on our Economics webpageLink opens in a new window.

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For MSc students, assessment is through formal examinations, coursework and an individually supervised research dissertation.

Feedback is a vital part of the assessment process, as it helps you to reach your full potential by identifying the strengths and weaknesses of your work and the actions needed to develop your understanding and enhance your performance.

Feedback is provided in a variety of ways, including: grades and comments on marked work, solutions to problem sets, verbal feedback from tutors and peers in classes, Advice and Feedback hours with academic staff and personal tutor meetings.

Reading lists

If you would like to view reading lists for current or previous cohorts of students, most departments have reading lists available through Warwick Library on the Talis Aspire platformLink opens in a new window. 

You can search for reading lists by module title, code or convenor. Please see the modules tab of this page or the module catalogue. 

Please note that some reading lists may have restricted access or be unavailable at certain times of year due to not yet being published. If you cannot access the reading list for a particular module, please check again later or contact the module’s host department.

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2:1 undergraduate degree (or equivalent) specialising mainly in economics. This means you should have achieved a good standard in undergraduate courses in microeconomics and macroeconomics, with a First (or equivalent) in econometrics/economic statistics, at an intermediate level. 

We also expect a good standard achieved in mathematics taken at undergraduate level.

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  • Band A
  • IELTS overall score of 6.5, minimum component scores not below 6.0.

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There are no additional entry requirements for this course.

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The following basic structure applies to MSc Economics and Data Science:

Economics Analysis A or Economics Analysis B 

Economic Analysis will develop your understanding of the theoretical foundations of macroeconomic analysis, and how models can be used in policymaking and to help explain empirical evidence. Then you will learn microeconomic concepts in the areas of game theory, information economics and choice under uncertainty. You can choose Economic Analysis A, which is a more applied version, or Economic Analysis B, which is more technical and theoretically orientated. As a guide, we expect students opting for the B version to have obtained the equivalent of a First-Class mark (70%) in their undergraduate studies, though we will consider a request to take a B variant even if that prerequisite is not met.

Quantitative Methods: Econometrics B

The module provides students with a thorough understanding of material needed for empirical quantitative analysis, particularly applied econometrics. You will understand how to produce high quality empirical econometric analysis using cross-sectional, time-series, and panel data, and also learn to interpret critically empirical results.

Foundations of Data Science 

Analyses in all fields of economics nowadays make frequent use of large and detailed datasets ("big data"). The explosion in data access and availability opens many opportunities for applied research, as well as new challenges on how to handle, process, and extract meaningful conclusions from the data. The aim of the module is to introduce students to the R and Python programming languages and basic concepts of data science, and to provide a "hands-on" experience with economic data. The module lays the foundation for more advanced materials.

Machine Learning and Big Data in Economics

This module covers recent developments in statistical methods tailored to handle very large datasets. These include machine learning techniques, supervised and unsupervised learning methods, such as those used broadly in academia and industry. Students will learn about the properties of those methods, how to critically apply them to different economic applications, and obtain hands-on experience in implementing them with existing datasets.

Dissertation

You will have the opportunity to pose an interesting research question in economics and data science, to find the correct methods for analysing the question, including development of theoretical models and/or analysis of data where appropriate, and to write up your results independently