860-0033-00L  Big Data for Public Policy

SemesterSpring Semester 2021
LecturersE. Ash, M. Guillot
Periodicityyearly recurring course
Language of instructionEnglish
CommentOnly for Master students and PhD students.



Courses

NumberTitleHoursLecturers
860-0033-00 GBig Data for Public Policy2 hrs
Thu12-14ML F 39 »
E. Ash, M. Guillot

Catalogue data

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.
ObjectiveMany 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.
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.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits3 credits
ExaminersE. Ash, M. Guillot
Typegraded semester performance
Language of examinationEnglish
RepetitionRepetition only possible after re-enrolling for the course unit.

Learning materials

 
Main linkGithub page
RecordingSyllabus
Additional linksGithub folder
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

Places60 at the most
Waiting listuntil 26.02.2021

Offered in

ProgrammeSectionType
Comparative and International Studies MasterElectivesWInformation
Data Science MasterInterdisciplinary ElectivesWInformation
Doctoral Department of Humanities, Social and Political SciencesDoctoral and Post-Doctoral CoursesW+Information
Science, Technology, and Policy MasterSocial SciencesOInformation