Data Science Lab

IMT School for Advanced Studies

Falco J. Bargagli Stoffi


Overview:

This course will provide an introduction to R for data science and policy evaluation. During each class, a series of applications useful to develop autonomous research projects will be presented. At the end of the course you will be able to perform from scratch a data analysis in R.

Format:

Interactive approach: we will perform simple analyses during the lectures after the main concepts are introduced. Remote lectures are available at this link. A shared folders with mock datasets is available here. The exam will consist in an individual project/presentation using one or multiple tools introduced during the course.

Prerequisites:

R and R Studio should be installed before the first lecture. Here a breif guide on how to install both.



Schedule:

Date Topic Readings
Week 1
June 3rd
Introduction to Data Science and R
Slides | Lecture Notes | R Code
Peng and Matsui, 2017
Meng, 2019
Week 1
June 5th
Exploratory Data Analysis
Slides | Lecture Notes | R Code
Wickham and Grolemund, 2017
Week 2
June 10th
Data Modeling
Slides | Lecture Notes | R Code
Tibshirani, 1996
Meinshausen and B├╝hlmann, 2010
Week 2
June 11th
Predictive Analysis
Slides | Lecture Notes | R Code
Friedman et al., 1984
Breiman, 2001
Chipman, 2010
Week 2
June 12th
Causal Machine Learning
Slides | Lecture Notes | R Code
Athey and Imbens, 2016
Wager and Athey, 2018