This GitBook was written by David Backus, Sarah Beckett-Hile, Chase Coleman, and Spencer Lyon for a course at NYU's Stern School of Business. The idea is to give students experience with economic and financial data and introduce programming newbies to the benefits of moving beyond Excel. We use the Python programming language, specifically Python's data management and graphics tools. If that doesn't whet your appetite, we have a more elaborate sales pitch.
We designed the book to accompany a live class. We've tried to make it self-contained, but the written word is a poor substitute for the interaction you get in a classroom.
The book comes in multiple formats. You can access it on the internet. Or you can download (and print) a pdf file. The former comes with links, which we think is a huge advantage, and can be updated quickly, but if you like paper by all means try the pdf. All of them are available at
https://www.gitbook.com/book/nyudatabootcamp/data-bootcamp/details
Related course materials are available at
We welcome suggestions. Send them to Chase Coleman or Spencer Lyon. Or, even better, post an issue on our GitHub repository.
This is work in progress. We've written seven chapters so far, more are on the way.
This project was Glenn Okun's idea. He really should have done it himself, but we thank him for the idea and his ongoing support. Paul Backus, Hersh Iyer (MBA17), Matt McKay, Kim Ruhl, and Itamar Snir (MBA17) contributed technical support and applications. Ian Stewart provided his usual expert advice on teaching methods. You may also notice a family resemblance to Tom Sargent and John Stachurski's Quantitative Economics, a Python- and Julia-based course in dynamic macroeconomic theory. We thank them for their advice and encouragement.
This work is licensed under the Creative Commons Attribution 4.0 International License. The text of which can be found here, or, for more information about what it means, you should visit the Creative Commons website.