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.
Performance assessment as a semester course