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!
- Databases and SQL from Software Carpentry: An introduction to using databases and making SQL queries for programming novices with a quantitative background.
- A Simple Guide to Five Normal Forms: A 1983 vintage rundown of data normalization. Short, and informal, but understandable, and with a few good illustrative examples. Using ASCII art.
- SQLAlchemy ORM Tutorial: A pretty detailed introduction to the SQLAlchemy Object Relational Mapper, which lets you design software object classes in Python, while storing the data behind them efficiently in a database. Lots of good SQLAlchemy conference videos on YouTube too:
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.