These are slides from my talk at the Boston Open Data Science Conference, held May 30-31, 2015 at the Boston Convention and Expo Center. I focused on how open-source tools are helping data scientists like me shorten the time it takes to get from raw data to valuable insights, especially in the case of geospatial data analysis.
In particular, I walk through some examples of quick-and-dirty geo analysis in Python, working with Runkeeper and Cambridge GIS data and using the geopandas library. Below is a live version of the “Cambridge running paces” road map I use as an example in the talk, and you can access the Jupyter notebook source code for the talk and examples here (though unfortunately the examples are of limited utility without the accompanying Runkeeper data).
