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.

Recommender Systems support the decision making processes of customers with personalized suggestions. They are widely used and influence the daily life of almost everyone in different domains like e-commerce, social media, or entertainment. Quite often the dimension of time plays a dominant role in the generation of a relevant recommendation.

The Spark data processing platform becomes more and more important for data scientists using Python. PySpark - the official Python API for Spark - makes it easy to get started but managing applications and their dependencies in isolated environments is no easy task.

A common pattern in most data science projects I participated in is that it’s all fun and games until someone wants to put it into production. All of a sudden the crucial question is how to deploy your model, which version, how can updates be rolled out, which requirements are needed and …

Nowadays, Spark surely is one of the most prevalent technologies in the fields of data science and big data. Luckily, even though it is developed in Scala and runs in the Java Virtual Machine (JVM), it comes with Python bindings also known as PySpark, whose API was heavily influenced by …