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Big Data for Public Policy
Last Updated: 2026-02-05 15:40:54
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.
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.
General Information
- Language
- English
- Levels
- DR , MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Registration & Places
- Signup End
- 21.02.2020
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Big Data for Public Policy |
|
2 h weekly |
Offered In
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Doctoral Department of Humanities, Social and Political Sciences (More Information at: )
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