An often encountered Antipattern in Python code are complex queries composed with the help of string substitutions and concatenations. SQLAlchemy Core allows you to generate queries in a programmatic way for many SQL databases like BigQuery.

Feature engineering takes up a huge part in the work-life of a data scientist. Sometimes this doesn’t stop at features but also the target variable itself is transformed leading to all kinds of unexpected consequences. In this post, you will learn about common pitfalls, how a transformation can affect the error measure, the math behind it, and even how all this can be used to your advantage.

With the advent of Deep Learning (DL), the field of AI made a giant leap forward and it is nowadays applied in many industrial use-cases. Especially critical systems like autonomous driving, require that DL methods not only produce a prediction but also state the certainty about the prediction in order …

With the release of Spark 2.3 implementing user defined functions with PySpark became a lot easier and faster. Unfortunately, there are still some rough edges when it comes to complex data types that need to be worked around.

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.