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syllabus.yaml
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code: ECBS 5241
title: Coding for Economists
instructors:
- name: Miklós Koren
bio: Miklós Koren is professor of economics at CEU, senior research fellow at the Institute of Economics, and research fellow of the Centre for Economic Policy Research. His research focuses on how talent and technology jointly determine business success. Professor Koren has more than two decades of experience with data and coding. He is a certified Carpentries Instructor.
email: [email protected]
office: QS B510 (Vienna)
- name: Ulrich Wohak
bio: Ulrich Wohak is a PhD student in Economics at the Vienna University of Economics and Business. He has experience as an economist at UK Competition and Markets Authority (CMA) and the Austrian competition authority (BWB) , MSc Specialized Economic Analysis (Competition) at Barcelona Graduate School of Economics (BGSE), MA Economics and Management at University of Aberdeen (UoA), worked and studied abroad for 7 years, continuous voluntary work as a paramedic, experienced with Stata, R, Python (quant) and ArcGIS.
email: [email protected]
office: TBA
credits:
us: 2
ects: 4
module: Analytical Foundations Module
drop: As required in the MA in Economics regulations
term: Winter 2021-2022
level:
- Master's
prerequisites:
- ""
description:
content: This course teaches how to organize data and code on your computer, how to write simple programs in Python to automate tasks, and how to use Stata throughout the steps of the your research process.
relevance: Quantitative research in economics and other social science requires an effective use of computational tools. The tools and methods used in this course will be applied throughout other courses and the students' professional career.
learning_outcomes:
- Understand folder structure. Perform operations in the command line on files in different folders.
- Automate repeating tasks with Python and Stata scripts, using for loops and functions.
- Read and write data in various formats in Stata. Explore data and fix common data quality errors.
- Filter, aggregate, reshape, and combine data for analysis.
learning_areas:
- type: Critical thinking
outcomes:
- Evaluate and compare different computing tools and methods.
- type: Quantitative reasoning
outcomes:
- Explore and analyze a large number of observations from potentially many different sources.
- type: Technology skills
outcomes:
- Write short programs in Python and Stata.
- Install and use packages in Python and Stata.
- "Use key programming tools in Python and Stata: functions, loops."
- type: Interpersonal communication skills
outcomes:
- Convey technical concepts verbally.
- Collaborate with others on technical tasks.
- type: Management knowledge and skills
outcomes:
- Create software with many components.
- Organize work components effectively.
- Meet deadlines.
- type: Cultural sensitivity and diversity
outcomes:
- Work together with students of different backgrounds.
- type: Ethics and social responsibility
outcomes:
- Understand ethical and legal constraints of acquiring data.
- Apply good practices of data protection.
readings:
required:
- "Paarsch and Golyaev, 2016. A Gentle Introduction to Effective Computing in Quantitative Research: What Every Research Assistant Should Know. MIT Press. (PG henceforth)"
- Koren, Miklós, Arieda Muço and András Vereckei, 2019a. “Introduction to the Command Line for Economics [website].” The Carpentries. https://datacarpentry.org/shell-economics/ (KMV_shell henceforth)
- Koren, Miklós, Arieda Muço and András Vereckei, 2019a. “Economics with Stata [website].” The Carpentries. https://datacarpentry.org/stata-economics/ (KMV_Stata henceforth)
recommended:
- "Sargent, Thomas J. and John Stachurski. 2020. Python Programming for Economics and Finance [website], https://python-programming.quantecon.org/index_toc.html"
- "Gentzkow, Matthew and Jesse M. Shapiro. 2014. Code and Data for the Social Sciences. https://web.stanford.edu/~gentzkow/research/CodeAndData.pdf"
teaching_methods:
- This is a participatory, experiential course. Students participate in live coding together with the instructors. This format facilitates quick and frequent feedback and enables students to achieve mastery in their computing skills.
assessments:
- description: Class participation
weight: 40
- description: Take-home coding assignment
weight: 60
technical_requirements:
- Personal laptop computer with administrative privileges to install open source software.
- "Operating system: Windows 10+ or Mac OS X 10.8+, or Linux 2.6.18+"
- git bash (Windows only), https://gitforwindows.org/
- Miniconda Python 3.8 distribution (note the version), https://docs.conda.io/en/latest/miniconda.html
- Stata 16, free teaching licenses available upon request.
- Internet access.
outline:
- session: 1
topic: Introduction to the command line
reading: PG 2.1-2.3, KMV_shell, Episode 1.
- session: 2
topic: Navigating files and directories
reading: KMV_shell, Episodes 2-3
- session: 3
topic: Python fundamentals
reading: PG 4.1-4.2
- session: 4
topic: Breaking up code into functions
reading: PG 4.3.7
- session: 5
topic: Repeating tasks with for loops
reading: PG 4.3.9-11
- session: 6
topic: Storing your data in lists and dictionaries
reading: PG 4.3.3
- session: 7
topic: Reading and writing files in different formats
reading: PG 4.8
- session: 8
topic: Data cleaning in Stata
reading: KMV_Stata, Episodes 1-2
- session: 9
topic: Transforming and combining data
reading: KMV_Stata, Episodes 3-4
- session: 10
topic: Save and Reuse your Work in .do Files
reading: KMV_Stata, Episode 5
- session: 11
topic: Repeat tasks with for loops
reading: KMV_Stata, Episode 6
- session: 12
topic: Finding and installing user-written Stata packages
reading: ""