How to Run A-B Tests as a Data Scientist

Explore the world of A/B testing for data scientists in this 28-minute video tutorial. Dive into the concepts of Bayesian testing, comparing it with the frequentist approach using real data and code. Learn how to define experiments, collect and process data, and implement both frequentist and Bayesian approaches. Discover the process of generating priors and posteriors in Bayesian testing, and gain insights into interpreting results. Examine the advantages and differences between Bayesian and frequentist methods, providing a comprehensive understanding of A/B testing techniques for data-driven decision making.

Introduction
Define the Experiment
Data Collection
Data Processing
Experiment: The Frequentist Approach
Experiment: The Bayesian Approach
Bayesian: Generating Priors
Bayesian: Generating Posteriors
Interpreting results
Bayesian Vs Frequentist