Valuing assets, modeling yield curves, analyzing risk, and building quantitative strategies: this is the ambition of the Finance option, which trains engineers who combine solid financial theory, applied econometrics, and advanced machine learning expertise.<\/strong><\/p>\n Quantitative finance requires engineers who can master economic and financial theory, rigorous statistical and econometric methods, and the latest computational tools. The growing role of machine learning<\/strong> and artificial intelligence<\/strong> in asset management, valuation, and risk analysis further reinforces the need for strong profiles across all three dimensions.<\/p>\n The Finance option of the Data Science for Economics and Finance program builds this quantitative foundation on solid economic science principles: financial theory, monetary macroeconomics, corporate finance, and network analysis. This economic foundation, combined with in-depth training in econometrics<\/strong> (advanced time series, panel data, choice models) and advanced education in machine learning and deep learning\u2014including NLP and neural networks\u2014produces quant<\/em> analysts with strong critical thinking skills. They are capable of questioning model assumptions and contextualizing their results. The curriculum also covers asset pricing, stochastic calculus, and yield curve modeling. Training in bank risk management and scoring<\/strong> completes the program, providing students with the tools needed to seize internship and job opportunities in this field, should professional prospects demand it.<\/p>\n