Autumn Semester 2020 takes place in a mixed form of online and classroom teaching.
Please read the published information on the individual courses carefully.

860-0033-00L  Big Data for Public Policy

SemesterSpring Semester 2020
LecturersE. Ash, M. Guillot
Periodicityyearly recurring course
Language of instructionEnglish
CommentOnly for MSc STP, MSc CIS, PhD students D-GESS and D-MTEC.
STP students have priority.


AbstractThis 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.
Objective
ContentMany 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.