Educational Resources

A collection of resources we’ve found helpful in our work, that will hopefully help you make use of the tools we’re building.

General Analytical Computing:

  • Software Carpentry: A set of tutorials and resources aimed at introducing folks with a scientific or analytical background to good standard computing practices, data hygiene, database usage, source code revision control systems, etc.
  • Data Carpentry: A sister project to Software Carpentry, focused more specifically on spreading best practices in data collection, archiving, organization, and analysis.
  • Good Enough Practices in Scientific Computing: A whitepaper from the organizers of Software Carpentry on good habits to ensure your work is reproducible and reusable — both by yourself and others!


Full Example Analyses:

  • A Large Data Workflow with Pandas: Using pandas, sqlite, and a giant CSV file containing information about 311 calls in NYC to do some exploration and data visualization in Plotly.

Basic Python Tools:

  • Structuring your Python Project: How to organize a Python project into modules that are packaged for easy interpretation by users. It includes a dummy package available via GitHub.