Best practices for empirical research in economics.
What best practices we can learn from software engineers?
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Economics and software engineering complement each other in understanding complex data.
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This course is an immersive simulation game using the statistical concepts learned earlier to argue about patterns of gender discrimination in the workplace. The focus is on interpreting and communicating data analysis results rather than performing statistical calculations. Various assignments, including readings, essays, and presentations, reinforce the learned concepts.
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This course guides you through the process of empirical research in applied microeconomics, from selecting research ideas and appropriate methods, through collecting data, to submitting your final paper to a journal. We won't talk about these steps, we will do these steps together. Each student and instructor completes a paper during the two terms of the course, which they submit at the end of the course to peer-reviewed journals.
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The course introduces two building blocks of macroeconomic modeling: forward-looking dynamic models and general equilibrium with heterogeneous agents. These tools are applied to problems of economic growth, labor market search, and industry dynamics. Quantitative model solutions are also illustrated using the Julia programming language.
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Data engineering is increasingly important to leverage the value created by data scientists and analysts. Executives who understand the basics of data engineering can help their team create data products that are easy to change in response to ever changing business requirements. This course offers a high-level overview of the types of decisions data engineers have to make, and a hands-on illustration of the most common problems on real-world data. The key goal of this course is to help executives make decisions about the data analytics efforts of their business and ask the right questions from their team. This will help increase the Return On Investment of analytics projects so that data can serve as a competitive advantage of the business.
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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.
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