This program requires internet access, that's where the data comes from.S&P 500 ETF (Spyders) (data from Yahoo finance).Fama-French equity "factors" (data from Ken French's website).GDP per capita in selected countries (data from the World Bank).We think it's best to take this one step at a time, but if you're interested in the logic behind the code, we give links to relevant documentation under "References." The occasional "Comments" are things for us to follow up on, we suggest you ignore them. In the meantime, you might note for future reference things you run across that you'd like to understand better. The code will be obscure if you're new to Python, but we will fill in the gaps over time. In this Notebook we illustrate some of the possibilities with examples. (And yes, Python and IPython are different things, but ignore that for now.) A more complete collection of materials, including this IPython Notebook, is available in our Github repository. This is an introduction to Data Bootcamp, a (prospective) course at NYU designed to give students some familiarity with (i) Python and (ii) economic and financial data. Data Bootcamp 1: Examples Python applied to economic and financial data
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