860-0033-00L Big Data for Public Policy
|Semester||Spring Semester 2020|
|Lecturers||E. Ash, M. Guillot|
|Periodicity||yearly recurring course|
|Language of instruction||English|
|Comment||Only for MSc STP, MSc CIS, PhD students D-GESS and D-MTEC. |
STP students have priority.
|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.