The course is designed with the practitioner in mind who chooses to employ advanced and novel methodologies to large datasets. Examples will be drawn from the growing economics literature which utilizes these methods, with a bias towards data-rich areas in empirical micro-economics.
The course will also rely on a series of demonstration exercises in the R programming language using real data and will give participants the opportunity to run and see the methods in practice. The provided code will be a useful starting point for participants to start using the methods discussed in the course in their own later work. The hands-on demonstrations will also familiarize participants with common sources of Big Data in economics: administrative data from public records, scanner data from retail transactions, and customer behavior data used to evaluate a marketing intervention. The course will also briefly introduce participants to other popular analytic tools from data science such as Jupyter notebooks.
For more info, view the Course Outline.
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