Testing Python Data Science Code [eLearn]
Tebeka, Miki2022
Streaming Video
Explore how to test scientific (data science) code written in Python.The larger and more complex the world of data science becomes, the more data there is to collect, sort, clean, model on, and much more. An emerging pain point in this brave new world is that a lot can go wrong if your data engineering and development practices are shoddy. This advanced-level course shows data scientists, Python developers, and data analysts how to test scientific (data science) code written in Python. Veteran data science trainer and consultant Miki Tebeka covers testing techniques, with a focus on issues specific to data science code, such as floating point errors, statistical testing, working with large datasets, choosing a baseline, and more. After presenting a testing overview, Miki dives into testing with pytest and hypothesis. He explains how to use schemas, truth values, approximate testing, and more in data validation. Miki goes over regression testing, then demonstrates how to test Jupyter Notebooks.
Main title:
Testing Python Data Science Code [eLearn] / with Miki Tebeka
Author:
Tebeka, Miki, speakerlinkedin.com (Firm)
Imprint:
Carpenteria, CA linkedin.com, 2022.
Notes:
9/01/202212:00:00AM
Performers:
Cast: Presenter: Miki Tebeka
System details:
Latest version of the following browsers: Chrome, Safari, Firefox, or Internet Explorer. Adobe Flash Player Plugin. JavaScript and cookies must be enabled. A broadband Internet connection.
LC class:
LDC2477020
Language:
English
BRN:
766758
Electronic access: