Applied Microeconomics for Policy, Using Python
This will be fast-paced class, requiring a substantial amount of time invested outside of class in mastering basic coding and data analytical skills, through simple but progressively more demanding exercises. The class will teach key economic concepts by creating demonstrations and simulations using Python and its data science-oriented data visualization and analysis packages. It will teach economic concepts by showing how these concepts can be usefully applied to real world data and policy. The Python software tools are components in the most widely used, non-proprietary, open data science software platform, and readily allow access to excellent visualization, statistical, and econometric analysis tools capable of handling even the largest public datasets. The same software platform can also be integrated with, and run, R and Stata statistical analysis code. The intention is to make this class doubly valuable to a student interested in public policy. First, the class will introduce you to cutting edge computer software tools that can be applied to real data for practical policy purposes (and hopefully both give you some advantages in post-graduation job markets, and facilitate future acquisition of even more advanced skills over the rest of your careers). Second, the class is designed to motivate learning economic concepts by showing that they can be simply and practically applied to real world data, and to give you some first-hand experience in doing this this. Much of the learning will be structured as completion of simulation or data analysis exercises. In addition to these exercises, every student will take an individual midterm examination, and join in a small group analysis project, with in-class presentation, discussion, and critique. Every student will also submit an individual final empirical economic project, in the form of a Jupyter (interactive Python) notebook. The Python data science software platform is increasingly being used by organizations and businesses, as well as researchers, to undertake policy-relevant analysis. Examples of some interesting and useful Jupyter notebooks documenting policy relevant data analysis reported by online journalists are at https://github.com/BuzzFeedNews . For examples of research economists doing relatively advanced analyses in Jupyter notebooks, see https://quantecon.org/notebooks . For an interested curated collection of Jupyter notebooks, see https://github.com/jupyter/jupyter/wiki/A-gallery-of- interesting-Jupyter-Notebooks . For a useful collection of Jupyter notebooks focused on introductory Python programming, see https://github.com/leriomaggio/python-in-a-notebook . The Jupyter notebooks in these archives can also give you valuable insights on how to do useful things when analyzing and visualizing large scale data. The class does not require a previous economics course. If you do have some economics experience, you will be encouraged to assist those of your peers who do not. Lectures will be based on interactive Python notebooks (aka Jupyter notebooks). Students will follow along class lectures using open source data science software installed on a personal laptop computer (Windows, Mac, or Linux). All students must read all assigned reading, since this will be assumed as background to Jupyter notebook content for economic concepts we go through in class. There are no computer programming prerequisites, but you will need to bring to class a personal computer with the Anaconda distribution of Python installed (more specific instructions will be distributed prior to the first class).