860-0033-00L Data Science for Public Policy: From Econometrics to AI
| Semester | Spring Semester 2024 |
| Lecturers | S. Galletta, E. Ash, C. Gössmann |
| Periodicity | yearly recurring course |
| Language of instruction | English |
| Comment | Only for Master students and PhD students. |
| Abstract | This course provides an introduction to big data methods for public policy analysis. Students will put these techniques to work on a course project using real-world data, to be designed and implemented in consultation with the instructors. | |||
| Learning objective | Many policy problems involve prediction. For example, a budget office might want to predict the number of applications for benefits payments next month, based on labor market conditions this month. This course provides a hands-on introduction to the "big data" techniques for making such predictions. | |||
| Content | Many policy problems involve prediction. For example, a budget office might want to predict the number of applications for benefits payments next month, based on labor market conditions this month. This course provides a hands-on introduction to the "big data" techniques for making such predictions. These techniques include: -- procuring big datasets, especially through web scraping or API interfaces, including social media data; -- pre-processing and dimension reduction of massive datasets for tractable computation; -- machine learning for predicting outcomes, including how to select and tune the model, evaluate model performance using held-out test data, and report results; -- interpreting machine learning model predictions to understand what is going on inside the black box; -- data visualization including interactive web apps. Students will put these techniques to work on a course project using real-world data, to be designed and implemented in consultation with the instructors. | |||
| Lecture notes | https://github.com/gochristoph/data-science-for-public-policy-2024 | |||
Competencies |
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