Data Science

The European Centre for Medium-Range Weather Forecasts (ECMWF) allows you to access its vast set of model data via a free account and a Python 3 package they provide as well. Unfortunately, a download limit of 30GB prevents the user to retrieve all public data. To circumvent this restriction, I wrote the package ecmwf_retrieve, which chops your request in small parts, sends all of them to the API of the ECMWF, and joins the retrieved data into one single file. »

The one thing a data scientist is needing most is, of course, data itself. Vast and well-formatted data. If you are interested in climatological one from Germany, the DWD will provide you with tons of files by the means of their FTP server. To ease the task of downloading, importing, and converting all the data into R, I wrote a package called dwd2r. »

In the second part of this series I want to introduce you to the huge ecosystem of spatial objects, their implementation in R, and how to display them in the most convenient way. »

In this series of blog posts I want to show you how to retrieve, handle, and visualize publicly available data on the example of the recent election for the Bundestag (the German parliament). »

Our award-winning contribution to the hackathon at the Datenspuren 2016. The task was to show how publicly available statistical data of the German city of Dresden could be used for the benefit of the general public. »