Learn the practical uses of A/B testing in Python to run and analyze experiments. Master p-values, sanity checks, and analysis to guide business decisions.
In this course, you will dive into A/B testing by learning to design, run, and analyze these A/B tests in Python. You’ll start by learning to define the right metrics before estimating the appropriate sample size and duration to yield conclusive results. Throughout this course, you’ll use a range of Python packages to help with A/B testing, including statsmodels, scipy, and pingouin. By the end, you will be able to run checks that guarantee accurate results, master the art of p-values, and analyze the results of A/B tests with ease and confidence to guide the most critical business decisions.
In this course, you will dive into A/B testing by learning to design, run, and analyze these A/B tests in Python. You’ll start by learning to define the right metrics before estimating the appropriate sample size and duration to yield conclusive results. Throughout this course, you’ll use a range of Python packages to help with A/B testing, including statsmodels, scipy, and pingouin. By the end, you will be able to run checks that guarantee accurate results, master the art of p-values, and analyze the results of A/B tests with ease and confidence to guide the most critical business decisions.
