COURSE CURRICULUM

This master’s program in data-driven economics is designed to equip students with advanced analytical and quantitative skills necessary for success in modern economics and finance. The curriculum combines core economic theory with cutting-edge techniques in data science, machine learning, and econometrics to analyze complex datasets and extract meaningful insights. In the first semester, students build a strong foundation in microeconomics, macroeconomics, econometrics, and advanced quantitative methods, developing the theoretical knowledge required to understand economic systems and decision-making processes. The second semester emphasizes practical applications of data analysis through modules in machine learning methods, big data analytics, and applied econometrics, enabling students to apply computational and statistical techniques to real-world economic problems. Optional modules allow students to further specialize in areas such as advanced economic theory, data science principles, and the mathematics underlying data analysis. Across the academic year, students also engage in research methods training to develop rigorous, independent research skills that underpin empirical investigations and policy analysis. By integrating theoretical understanding with practical, hands-on experience in handling large datasets and applying quantitative methods, this program prepares graduates for careers in finance, economic consultancy, policy-making, data analytics, and advanced research, equipping them with the expertise to address complex challenges in a data-driven economic environment.