Big Data in Economics

4.0 credits

This course introduces modern economic and machine learning methods for analyzing big data. Topics include statistical learning, linear and nonlinear regression, classification, resampling methods, model selection and regularization, tree-based methods, support vector machines, clustering, network analysis, and double/debiased machine learning. The course emphasizes both the statistical foundations and practical implementation of these methods. Throughout the course, students will apply these techniques to real-world economic problems, including prediction, causal inference, and market analysis. The course also develops programming skills and provides hands-on experience implementing selected algorithms in code.