Eight years after my original post on building data science teams, the landscape has transformed dramatically. From the experimental “unicorn hunting” of 2017 to today’s systematic, specialized team construction, here’s what’s changed and what works in 2025.

A comprehensive guide for data professionals to improve how they present data visually. Talk from PyData Berlin 2018.

In the German blog post, I analyze voting patterns of German parliament members using machine learning techniques. By encoding votes as numerical vectors (Yes=1, No=-1, Abstention=0), I apply PCA and t-SNE dimensionality reduction to visualize political clustering, revealing clear faction groupings and identifying potential party outliers in the Bundestag.

As you may know I regularly work for Data Science Retreat - a boot camp style program, where participants (with prior experience) are trained to be freshly minted data scientists. Over the course of three months they also build a (larger) portfolio project that are presented on a demo day.

On …

Given a unbiased coin, can you model the Bernoulli distribution with any probability $p$?

Extra: How do we go back?

In this talk about Jupyter Notebooks for beginners, I provided a comprehensive tutorial on the tool, covering everything from basic setup to advanced tricks and the plugin ecosystem. Given at PyData Berlin 2017.

In the times of full-stack engineers, developers have to be as good at algorithmic problem solving as in designing. It is often the case that a programmer has a low talent in designing a good UX. This guide was written for those developers in mind. Let me introduce patterns of …