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After looking at the views of your website, you suspect there might be a sudden change in the number of views after a certain date.

If there is indeed a date when the number of views changes suddenly, how do you find that date?

Wouldn’t it be nice if you can leverage observed data and some beliefs to guess the change point with high certainty? That is when Bayesian inference comes in handy.

In my latest article, we will learn what Bayesian inference is and how to use PyMC3 to perform Bayesian analysis.

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For data scientists, to make your code reproducible, you need to put them into functions and classes, but the code may break because of some functions. Even if your code doesn’t break, how do you know if your function will work as you expected?
In general, you should use testing for your data science projects because it allows you to:

Make sure the code works as expected
Detect edge cases
Feel confident to swap your existing code with improved code without being afraid of breaking the entire pipeline

There are many Python tools available for testing, but the easiest tool is pytest. I like pytest because it helps me to write tests with minimal code. If you were not familiar with testing, pytest is a great tool to get started.
In this article, I provided some simple examples and short explanations to get you started.Favorite