A program in advanced econometrics and applied economics that leverages data science and machine learning tools to analyze markets, evaluate public policies, and inform decision-making in key areas.<\/strong><\/p>\n The rigorous evaluation of public policies, understanding labor market dynamics, analyzing regional inequalities, and measuring the effects of the environment on health: these are all issues in which the quantitative economist<\/strong> plays a central role. In a world that generates massive amounts of data and seeks causal explanations\u2014not just predictions\u2014mastery of applied econometrics remains the foundational skill, now enriched by the contributions of machine learning<\/strong> and artificial intelligence<\/strong>.<\/p>\n The Economics track of the Data Science for Economics and Finance program provides in-depth training in econometrics and economic analysis, complemented by a solid foundation in data science and machine learning. It covers a broad spectrum of applied fields: labor economics, public economics, environmental economics, urban economics, demography, and health economics. It draws on a wide range of advanced econometric methods\u2014time series, panel data, discrete choice models, spatial econometrics, and causal inference\u2014which are complemented and reinforced by modern tools in machine learning and network analysis.<\/p>\n This program opens the door to a wide range of career opportunities in economic decision-making<\/strong>, whether in the public sector<\/strong> (ministries, statistical institutes, social security agencies) or the private sector<\/strong> (research firms, think tanks, research organizations, pharmaceutical companies, and consulting firms).<\/p>\n