University of Hull

What you'll study

This programme mixes core training in economic theory and econometrics with practical modules in data science and artificial intelligence. You will learn to prepare, analyse and model large and complex economic datasets, using programming tools and machine-learning methods to address real-world economic questions.

• Core quantitative economics and econometrics – advanced microeconomics and macroeconomics concepts focused on empirical applications, time series and panel-data techniques, and causal inference methods tailored to economic research.
• Programming and data-management – hands-on training in languages and tools commonly used in data science (for example Python or R), databases, data cleaning, visualisation and reproducible workflow techniques.
• Machine learning and AI for economics – supervised and unsupervised learning, model selection, regularisation, neural networks and their use in forecasting, classification and clustering tasks relevant to economic data.
• Big data and computational methods – techniques for working with large-scale datasets, high-performance computing concepts, and practical approaches to scale algorithms for policy and business datasets.
• Applied projects and case studies – sector-focused applications such as labour-market analytics, financial econometrics, public policy evaluation, and market research using real or simulated datasets.
• Independent research dissertation – a substantial project where you apply data-science and AI methods to an economic research question under academic supervision.

The programme typically comprises taught modules delivered through lectures, seminars and computer-lab practicals, followed by an independent dissertation project that emphasises applied analysis and interpretation for economic decision-making.

Entry requirements

Applicants should normally hold a good undergraduate degree in economics, mathematics, statistics, computer science, or a closely related quantitative discipline. Candidates with a second-class degree and substantial relevant work experience or additional quantitative training may also be considered.

Applicants are expected to demonstrate numeracy and basic programming familiarity; evidence of prior study in econometrics, statistics or computational methods is advantageous. International applicants must meet the University of Hull's postgraduate English language requirements.

Career prospects

Graduates are prepared for roles that combine economic insight with data-science expertise. Typical career paths include:

• Data scientist or data analyst in financial services, tech firms, consultancies and market-research organisations.
• Economic analyst, policy analyst or research officer in government departments, central banks and international organisations.
• Quantitative analyst or risk analyst in finance and insurance sectors.
• Machine-learning engineer or applied researcher working on pricing, forecasting, demand modelling or evaluation of public-policy interventions.
• Continuation to doctoral study (PhD) in economics, data science or related interdisciplinary fields for those pursuing academic or advanced research careers.

Why study at University of Hull

The University of Hull offers a supportive environment with research-active staff in economics and strong links to applied data-analysis work. The programme benefits from practical teaching that emphasises hands-on computing and real-data projects, helping you build a portfolio of applied analyses suitable for employers.

The campus provides teaching and computing facilities for postgraduate students, and the University’s careers and enterprise services help connect students with local and national employers, internships and placement opportunities. Studying at Hull also gives access to multidisciplinary expertise across economics, mathematics and computing, enabling a well-rounded preparation for data-focused economic careers.