Master of Science in Economics with Data Science

Program overview

This full-time, four-semester master's programme builds a bridge between advanced economic theory and modern data science. Taught in English, it combines a rigorous theoretical base with practical training in analytical tools so students can apply quantitative methods to economic questions. The course is aimed at students who want to work at the intersection of economics and data-driven technologies, equipping them with both conceptual knowledge and hands-on experience.

Curriculum and skills

A core feature is the deliberate integration of data science and AI into economic analysis — from established machine learning techniques to newer generative AI approaches. Students learn how to gather and structure large datasets, implement and interpret AI-driven simulations of economic models, and generate data-based forecasts and empirical insights. The programme’s focus on emerging technologies prepares graduates to address practical policy and business problems and to adapt as analytical tools and industry needs evolve.

Key facts and requirements

• Duration and mode: 4 semesters, full-time
• Language of instruction: English
• Academic focus: Economics blended systematically with Data Science and AI
• Practical skills gained: large-scale data collection and structuring, machine learning and generative AI methods, AI-driven simulation of economic models, empirical analysis and forecasting
• Outcome orientation: training to tackle real-world economic problems and adapt to changing technological and industry landscapes

Program structure and key modules The program begins with a concentrated first semester of five compulsory modules that establish the programme’s methodological and theoretical backbone: Foundations in Economics with Data Science, Principles of Data Science, Microeconomics, Macroeconomics and Econometrics. These core courses ensure all students share a solid grounding in economic theory, statistical inference and computational methods before moving into specialised work.

During the second and third semesters the curriculum becomes highly customizable. Students take electives (and at least one elective is also required in the fourth semester) drawn from a mix of advanced economics and data science/AI offerings. Core elective components include a Seminar Module—designed to strengthen academic research and writing on specialised topics—and an Empirical Research in Economics module that gives hands-on experience in applied projects using modern data-science techniques. Empirical projects frequently involve teamwork, may include collaboration with external partners, and culminate in both presentations and scientific posters under the joint supervision of two professors.

Flexibility and final project The Free Elective Area lets students choose seven modules from a broad catalogue (examples: computational economics, algorithmic game theory, machine learning for economists, causal inference, forecasting, big-data infrastructure and responsible AI). One of those seven can be swapped for a second seminar, and study-abroad credits can be applied here to support an individualised study profile. A further compulsory module appears in the third semester to allow for additional specialisation options (for instance, a BRICS certificate or language courses). In the fourth semester students write the Master’s thesis, demonstrating the ability to apply analytical and methodological skills—often employing advanced data-science or AI approaches—to a substantive economic research question.

Requirements (curriculum at a glance)

• First semester: Five compulsory modules — Foundations in Economics with Data Science; Principles of Data Science; Microeconomics; Macroeconomics; Econometrics.
• Electives: Taken across semesters 2 and 3 (with at least one elective also required in semester 4).
• Seminar Module: Focus on specialised topics, research and academic writing.
• Empirical Research in Economics: Practical applied-research projects using modern data-science methods; teamwork, potential external partners; supervised by two professors; assessed via presentations and scientific posters.
• Free Elective Area: Select seven advanced modules from economics and data-science/AI topics; option to replace one module with a second seminar; study-abroad modules may be credited.
• Third semester additional compulsory module: Enables specialisation options such as BRICS certificate or language courses.
• Fourth semester: Master’s thesis demonstrating ability to apply analytical and data-science/AI methods to a complex economic question.

Learning outcomes (what you will gain)

• Strong theoretical and methodological foundation across economics and data science.
• Practical competence in empirical research design, data analysis, and scientific communication (oral presentations and posters).
• Experience working in teams and, where applicable, with external or international partners—cultivating self-organisation and intercultural collaboration.
• Specialist technical skills in areas such as machine learning, causal inference, forecasting and big-data tools depending on elective choices, and the ability to apply these methods to complex economic problems in the Master’s thesis.

Graduates are equipped for data-driven roles that require both economic reasoning and advanced analytical skills. Typical career paths include economic/data analyst, quantitative analyst, economic consultant, policy analyst, and roles in finance, tech firms, research institutes, and international organisations where modelling, forecasting and machine learning are applied to economic problems.

The programme’s research-oriented modules and a thesis involving empirical or AI-driven methods also prepare graduates for further academic work or doctoral studies in economics, econometrics, or data science-related fields.