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860-0033-00L 3 Credits DR , MSC D-ITET , D-INFK , D-MATH , D-GESS

Data Science for Public Policy: From Econometrics to AI

Lecturers: Prof. Dr. Elliott Ash
Examiners: Prof. Dr. Elliott Ash, Dr. Sergio Galletta, Dr. Claudia Marangon
Does not take place this semester. Only for Master students and PhD students.
VVZ CR n/a

Last Updated: 2026-06-03 00:14:12

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.

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.

Resources

Lecture Notes

https://github.com/ClaudiaMarangon/data-science-for-public-policy-2025

Learning Materials (Links)

General Information

Language
English
Levels
DR , MSC
Frequency
Yearly recurring

Examination

Type
graded semester performance

Registration & Places

Max Places
60

Course Components

Type Title Time & Place Hours
lecture with exercise Data Science for Public Policy: From Econometrics to AI
Does not take place this semester.
No time listed 2 h weekly

Offered In