From the course: Testing Python Data Science Code [Update][Codespaces]
Testing scientific applications - Python Tutorial
From the course: Testing Python Data Science Code [Update][Codespaces]
Testing scientific applications
- [Instructor] Testing is an important part of the development process. Test gives us a safety net to make sure we don't break one thing while fixing another. However, scientific applications pose several unique challenges in testing. Hi, I'm Miki Tebeka. I've been developing with Python for the last 25 years and spend a lot of my time help research code make it to production. Making code production-ready involves a lot of testing, and I have firsthand experience with testing scientific applications. In this course, we look at the various ways you can test scientific code. We write tests, validate data, look at performance monitoring and talk about baselines for your algorithms. All of the methods you'll see come from my experience or from teams that I've worked with. Let's roll.
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