A/B Testing in Python Course | Design, Run, and Analyze Your Experiments Course

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 sanity 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.

Collaborators

Jasmin Ludolf

Prerequisites

Hypothesis Testing in Python

Principal Data Science Manager

Moe is a Principal Data Science Manager with 10+ years of experience working in the fields of data science and analytics in various settings including research, academia, and industry. He combines his physics/engineering domain knowledge with data science expertise to uncover insights in massive datasets, influence critical design decisions, and drive product improvements. Over his career, he has advised and built analytics and experimentation functions for several fortune 500 companies. He fuels his passion for Data Science/AI through teaching and giving invited lectures/talks. Moe has a PhD in Nuclear Fusion Engineering from UCLA with a focus on experimentation and computational analysis, and a BSc in Mechanical and Aerospace Engineering from the University of Illinois at Urbana-Champaign. His areas of expertise include energy systems, AI, autonomous driving, shipping high-impact products, and all things data and experimentation.

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