This documentation project is an attempt to bring structure to an unstructured activity.
According to most sources, data-analysis is a well-defined process in which specialists (data-analysts) clean, transform, model and question data for helping businesses make intelligent decisions.
Although there is a large body of work — both academic and industrial — addressing how to deal with data, there is a lack of documentation addressing the complexity of the overall data-analysis process and providing concrete advice on how to execute effectively.
The lack of documentation leads to large differences in how data-analysts operate and large differences in the effectiveness of those operations, which constitute a major issue both for analysts doing the work and for businesses aiming to benefit from the works of analysts in a consistent way.
This project lists a collection of recipes (do's and don'ts) for analysts to make them more effective in their data analysis engagements.
Listed recipes are grouped into the following categories, each of which is by itself a craft:
TODO: Link references to recipes.
References were highly influential for the core ideas presented here.