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A/B Testing in Python

Intermediate
Updated 12/2024
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.
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PythonProbability & Statistics4 hours16 videos51 exercises4,000 XP6,565Statement of Accomplishment

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Course Description

In this course, you will dive into the world of A/B testing, gain a deep understanding of the practical use cases, and learn to design, run, and analyze these A/B tests in Python.

Discover How A/B Tests Work



Did you know that you are almost guaranteed to participate in an A/B test every time you browse the internet? From search engines and e-commerce sites to social networks and marketing campaigns — all businesses hire the best data analysts, scientists, and engineers to leverage the power of AB testing. Testing different variants can help optimize the customer experience, maximize profits, inform the next best design, and much more.

Learn About A/B Testing in Python



You’ll start by learning how to define the right metrics before learning how to estimate 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 of the course, you will be able to run the necessary 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.

Prerequisites

Hypothesis Testing in Python
1

Overview of A/B Testing

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2

Experiment Design and Planning

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3

Data Processing, Sanity Checks, and Results Analysis

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4

Practical Considerations and Making Decisions

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A/B Testing in Python
Course
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