python

Using literate programming is a widespread practice amongst data scientists. This practice not only encourages data scientists to produce transparent, rich and reflective accounts of their analysis without the extra overhead of switching between tools, but also leads to artefacts (i.

While I have been using R for many years now (mainly for data manipulation and visualization), and I am extremely happy with some of its features (like how easy is to deal with data or to create interactive reports that can be exported in plenty of different outputs, such as pdf, documents, slides, dashboards or blog posts like this one).

Collaborative repository with Python scripts aimed to import data into OpenStreetMap + its documentation

There are two ways of constructing a software design: one way is to make it so simple that there are obviously no deficiencies; the other is to make it so complicated that there are no obvious deficiencies.