Being in the data science domain for quite some years, I have seen good Jupyter notebooks but also a lot of ugly. Notebooks can have the perfect balance between text, code and visualisations but how often do your notebooks rather get messy and incomprehensible after a while? Follow some simple best practices to work more efficiently with your notebooks.

This talk presented at the EuroPython 2016 introduces several Python libraries related to the handling of GPS data. The slides of this talk are available on Github or on nbviewer.

If you are doing probabilistic programming you are dealing with all kinds of different distributions. That means choosing an ensemble of right distributions which describe the underlying real-world process in a suitable way but also choosing the right parameters for prior distributions. At that point I often start visualizing the …

Often I get the question as a Data Scientist what the Python Data Science Stack actually is and where a beginner should start to learn. The Python ecosystem, especially around the topics such as data analytics, data mining, data science and machine learning is so vast and rich that it …