COURSE CURRICULUM

The MSc in Data Science for Econometrics at Erasmus School of Economics is designed to equip students with advanced quantitative and analytical skills essential for understanding complex economic phenomena. The program begins with foundational courses that introduce key econometric methods, including Microeconometrics and Bayesian Econometrics, enabling students to model and interpret data with precision. Simultaneously, students gain expertise in Machine Learning and Multivariate Statistics, which provide the tools necessary to analyze high-dimensional datasets and extract meaningful patterns. Core courses in Advanced Marketing Models and Computer Science for Business Analytics ensure that students develop a practical understanding of how data-driven techniques can be applied to solve real-world business and economic problems. Throughout the program, special attention is given to integrating theory with practice. The Seminar on Case Studies in Data Science for Econometrics allows students to work intensively on real-life data challenges in groups, applying the methodologies learned in earlier courses while refining their problem-solving and teamwork skills. In parallel, the Topics in Advanced Statistics course deepens students’ statistical knowledge, preparing them for rigorous data analysis in professional and research contexts. The master’s thesis is a key component of the program, spanning the last blocks of study, where students conduct independent research under the supervision of experienced faculty members. This project requires them to identify a relevant economic or business problem, apply appropriate data science techniques, and communicate their findings effectively, fostering both technical expertise and professional skills. Overall, the curriculum balances theoretical grounding with applied experience, preparing graduates for careers in economics, finance, consultancy, and data-driven roles, or for further academic research. By combining econometrics, machine learning, and business analytics, the program provides a robust framework for making data-informed decisions and contributing to the rapidly evolving field of quantitative economics.