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pandas is a Python library for data analysis that has become very popular in recent years. More specifically, pandas is an in-memory analytics tool that offers ...

Jupyter notebooks are ideal for exploratory data analysis. They have therefore become the de facto standard for exploratory data analysis and rapid ...

NumPy is the abbreviation for Numerical Python. Many Python packages for scientific functions use the NumPy array objects as one of the standard interfaces for ...

We will access remote file systems and geodata, read various serialisation formats such as CSV and JSON and provide data in suitable databases and interfaces ...

There are many different Python libraries that make it much easier to clean and validate data. We will use these libraries in practical examples to recognise ...

There are many Python libraries for visualising data, each with a different focus. This course will give you an overview of the various libraries and show you ...

The basic design principles are indispensable for both explorative and explanatory data visualisation. Visual hierarchies can be used to focus on specific ...

‘Single occurrences that cannot be reproduced are of no significance to science’ wrote Karl Popper in 1935 in The Logic of Research. This has not changed to ...

The Python for Data Science stack should be continuously adapted to current conditions and benefit from better data science workflows. In this workshop, we ...

Dashboards present the most important information for achieving one or more goals. They consolidate and organise the information so that it can be viewed at a ...