University of Warwick

What you'll study

This programme blends rigorous economics training with practical data-science methods. You take compulsory modules that establish a foundation in microeconomics, econometrics and statistical inference, alongside modules in machine learning, causal inference and computational methods for big data. Teaching mixes lectures, practical computer labs and a research-led dissertation or applied project using real-world data.

• Core economic theory and methods: advanced microeconomics, empirical microeconometrics, and applied macroeconomic techniques that provide the conceptual tools for policy and market analysis.
• Data-science and computational modules: supervised and unsupervised learning, deep learning basics for economists, time-series and panel-data methods, Bayesian statistics, and scalable computation (e.g. parallel processing, cloud-based workflows).
• Applied econometrics and causal inference: identification strategies, programme evaluation, instrumental variables, difference-in-differences, synthetic controls and modern approaches to estimating causal effects from observational data.
• Programming and data engineering: practical training in languages and libraries used in the field (such as Python or R), data wrangling, reproducible research practices and working with large datasets.
• Project / dissertation: an extended piece of empirical work, often undertaken with access to administrative, commercial or experimental datasets, supervised by academic staff. Projects typically emphasise end-to-end analysis: problem definition, data acquisition and cleaning, model development, interpretation and communication of results.

Entry requirements

Applicants are expected to have a strong quantitative undergraduate degree, typically a first-class or upper second-class (2:1) honours degree in economics, statistics, mathematics, computer science, engineering or a closely related discipline. Demonstrable training in calculus, linear algebra, probability and introductory statistics/econometrics is normally required.

• Quantitative background: prior coursework in calculus, multivariable mathematics, linear algebra and basic probability/statistics.
• Programming and data skills: evidence of experience with a programming language commonly used for data analysis (for example Python, R, MATLAB or similar) is expected or applicants should be prepared to acquire these skills before or at the start of the course.
• International applicants: equivalent academic qualifications are considered. Proof of English language proficiency may be requested (for example recognised tests or university-approved alternatives).
• Other considerations: relevant work experience, research experience or a strong statement of purpose explaining quantitative interests and career aims can strengthen an application.

Career prospects

Graduates leave equipped for roles where economic insight and data-science skills combine. Typical career paths include data scientist or machine-learning engineer roles within finance and technology firms, quantitative analyst positions in investment and risk teams, economic consultancy, policy analyst roles in government and international organisations, and research positions in applied economics and econometrics. Alumni also pursue further research (PhD) in economics, statistics or data science.

• Private sector: data scientist, quantitative analyst, product analyst, pricing or risk modeller.
• Consultancy and think-tanks: economic consultant, policy modeller, research analyst.
• Public sector and international organisations: policy economist, statistician, programme evaluator.
• Academia and research: research assistant, PhD candidate in economics, statistics or data science.

Why study at University of Warwick

Warwick offers this programme within a strong, research-active economics environment with close links to data-science expertise across the university. Students benefit from access to interdisciplinary research groups, computing facilities and a skills-focused curriculum that emphasises reproducible empirical work.

• Interdisciplinary support: the course builds on links between economics, statistics, computing and business, enabling supervised projects that draw on diverse datasets and methods.
• Research-led teaching: modules are taught by staff active in applied econometrics, machine learning and computational economics, giving students exposure to current techniques and applied problems.
• Employability and industry connections: career services, employer events and relationships with private and public organisations help students with placements, internships and job opportunities in data-driven roles.
• Practical facilities: students have access to computing clusters, software licences and training resources to develop scalable, production-ready approaches to data analysis.