K2SP 3685 - Data Analysis for Public Policy

K2SP 3685 · Semestre d'automne 2026-2027

Langue d'enseignement
English
Type d'enseignement
Cours magistral
Nombre d'heures
28
Crédits
4

Informations

Descriptif

This course is a professionally oriented data analysis program offered within the Economics and Public Policy stream at the School of Public Affairs. Its objective is to provide students with the technical skills needed to describe and critically assess the effects of public policies. To this end, students learn the basic coding skills required to handle large datasets using the R programming language. More specifically, they are taught how to import, clean, transform, visualize, and communicate insights from data. The course also emphasizes good practices in data visualization, including how to avoid misleading graphs and how to take a step back when interpreting them. By the end of the course, students should be able to work independently with real-world datasets and produce clear, effective visualizations for a broad audience, including public policy makers.

Students will learn how to conduct data analysis effectively for professional purposes by identifying the best available data sources online and developing appropriate ways to visualize and present their findings in reports tailored to their target audience.

Pré-requis

All students who are new to RStudio or to coding more generally are expected to complete the Data Camp training at the very start of the semester, in order to reduce heterogeneity in programming levels among students

Format pédagogique

24 hours lectures, complemented by 4 hours of tutorials in total (cohort divided into 2 groups, so 2 h tutorial per student) and 2 hours of IT clinics.

Mode de validation

-Midterm exam in class 50%

-Group presentation in class : 35% for the group’s presentation + 15% for discussion of the work of other groups

Charge de travail

Students are expected to answer to the quizzes (3 throughout the semester) and to prepare a midterm assignment at home (coding exercise with reflection questions),

They have to come with prepared questions for the Q&A sessions.

Lectures

Lectures principales 5

  1. - “Statistical Inference via Data Science: A ModernDive into R and the Tidyverse (Second Edition)” https://moderndive.com/v2/

  2. - Angrist & Pischke (2010, JEP), “The Credibility Revolution in Empirical Economics” https://www.aeaweb.org/articles?id=10.1257/jep.24.2.3

  3. - Introduction to Econometrics with R https://scpoecon.github.io/ScPoEconometrics/

  4. - Kieran Healy (2018) "Data Visualization, A Practical Introduction" https://socviz.co/index.html#preface

  5. - Schwabisch (2014, JEP) “An Economist's Guide to Visualizing Data” https://www.aeaweb.org/articles?id=10.1257/jep.28.1.209

Lectures secondaires 3

  1. - Carl. T. Bergstrom and Jevin D. West (2020) "Calling Bullshit, The Art of Skepticism in a Data-Driven World" https://callingbullshit.org

    Emprunter

  2. - Hadley Wickhman & Garett Grolemund, "R for Data Science", 2017, O'Reilly Media

    Emprunter

  3. - Kieran Healy, "Data Wrangling and Data Visualization with R", course website for a class taught at Duke University https://socdata.co/

Unités pédagogiques

N° d'UPType d'UP Unités pédagogiquesEnseignants Nombre d'heuresCrédits
18152Cours magistral Data Analysis for Public Policy Eleonore RICHARD244
18513Conférence Data Analysis for Public Policy Eleonore RICHARD20
18514Conférence Data Analysis for Public Policy Eleonore RICHARD20
20156Conférence A Data Analysis for Public Policy (project clinic) Eleonore RICHARD20

Enseignants

  • Eleonore RICHARD

Dans les maquettes

Contacts et ressources

Contact pédagogique
SAMINADIN Sophia