High Performance Data Analytics in Python

Scientists, engineers and professionals from many sectors are seeing an enormous growth in the size and number of datasets relevant to their domains. Professional titles have emerged to describe specialists working with data, such as data scientists and data engineers, but also other experts are finding it necessary to learn tools and techniques to work with big data. Typical tasks include preprocessing, analysing, modeling and visualising data.

Python is an industry-standard programming language for working with data on all levels of the data analytics pipeline. This is in large part because of the rich ecosystem of libraries ranging from generic numerical libraries to special-purpose and/or domain-specific packages, often supported by large developer communities and stable funding sources.

This module will give an overview of working with research data in Python using general libraries for storing, processing, analysing and sharing data. The focus is on high performance. After covering tools for performant processing on single workstations the focus shifts to profiling and optimising, parallel and distributed computing.

Prerequisites

  • Basic experience with Python

  • Basic experience in working in a Linux-like terminal

  • Some prior experience in working with large or small datasets

Software setup

Episodes

Reference

Walkthrough for module authors

Learning outcomes

This material is for all researchers and engineers who work with large or small datasets and who want to learn powerful tools and best practices for writing more performant, parallelised, robust and reproducible data analysis pipelines.

By the end of this module, learners should:

  • Have a good overview of available tools and libraries for improving performance in Python (link to leaves in skill tree)

  • Knowing libraries for efficiently storing, reading and writing large data (link to leaves in skill tree)

  • Be comfortable working with NumPy arrays and Pandas dataframes for data analysis using Python (link to leaves in skill tree)

  • …

Credit

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Copyright (c) 2025, EVITA project, Ashwin Vishnu Mohanan, Claudia Blaas-Schenner, Jaison Lewis, Jasper Seehofer, Kevin Lüdemann, Kjartan Thor Wikfeldt, Mary Kate Chessey, Matthias Eulert, Tobias Haas, Victoria Döller, Yinyin Ma, and Yonglei Wang

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