Lecturer: Uğur Aytun, Visiting Researcher at METU

Classroom: 01:40 PM-16:30 PM Friday, Computer-Lab

Office hours: 12:00 PM-01:00 PM Friday, A26-B

Course prerequisites: IS 100, ECON 206

Description

Data is a crucial resource for understanding and interpreting the world around us. Effectively harnessing this resource is essential for deriving meaningful insights. In economics, the importance of data has grown significantly with the proliferation of diverse sources, including administrative datasets, large-scale surveys, and social media data.

This course aims to introduce students to the modern data science toolkit. It covers the fundamentals of data manipulation, visualization, and key statistical techniques, such as regression analysis. The primary tool for this course is R, an open-source programming language widely used by economists. R is a powerful tool for data analysis and visualization, and it is an essential skill for any economist working with data. It also helps us to estimate big data with high-dimensional fixed effects.

Course objectives

Students will learn how to use R for economic analysis and the basic tools of data science. By using R and RStudio, students will be able to import, clean, manipulate, visualize, report, present, and analyze data. They will also learn how to write reports and create presentations using R Markdown.

Learning outcomes

Basic programming skills in R programming language

Accessing and importing data from various sources

Manipulating, converting and storing data using data.table, collapse, haven and fst packages

Data visualization using ggplot2

Statistical analysis using regression models with fixest package

Difference-in-differences and event study analysis

Poisson estimation with high-dimensional fixed effects

Perform reproducible research

Creating reports and presentations using R Markdown

Grading

The course consists of lectures, midterms, homeworks and projects.

Course grades will be based on 2 midterms (30 pts each), 1 project (40 pts), and forum participation (as a bonus, up to 10 pts). There will be no make-up.

The project teams will consist of 3 students. Projects will be presented on-line and be submitted by midnight, the same day.

Textbooks

Erol Taymaz, Introduction to Data Science, Lecture Notes

Venables, W. N., Smith, D. M. and the R Core Team (2015), An Introduction to R, R Core Team

Grolemund, Garrett, and Wickham, Hadley (2017), R for Data Science, O’Reilly.

Hanck, C., Arnold, M., Gerber,A. and Schmelzer, M. (2020), Introduction to Econometrics with R

Julian Hinz and Irene Iodice, Data Science for Economists, Lecture Notes

Grant R. McDermott, Data science for ecoonmists, Lecture notes

Nick Huntington-Klein, The Effect: An Introduction to Research Design and Causality

Scott Cunningham, Causal Inference: The Mixtape

Getting started with R and RStudio

In second week, please install R and RStudio on your computer. You can download R from here and RStudio from here.

I strongly recommend to use GitHub Copilot for R programming. But you should sign up to Github in advance.

Tools > Global Options > Code > Git > Enable GitHub Copilot.

Presentations