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Advanced Experimentation Course
This course is designed for practitioners who already understand the foundations of experimentation and want to dive deeper into the implementation methods, calculations, setup, and analysis of experiments.
9 Lessons 
Lesson 1
Read ArticleClient-side testing and personalization explained
An in-depth analysis of the most important technical considerations when implementing A/B tests and personalization campaigns on the client-side.
Lesson 2
Read ArticleServer-side testing and personalization explained
An in-depth analysis of the most important technical considerations when implementing A/B tests and personalization campaigns on the server-side.
Lesson 3
Read ArticleThe Frequentist Approach to A/B Testing
While still commonly used today, is the Frequentist Approach to statistical inference within A/B testing the most effective? Let’s dive into the math.
Lesson 4
Read ArticleThe Bayesian Approach to A/B Testing
Known for being less restrictive, highly intuitive, and more reliable, let’s dive into the math behind the Bayesian Approach to statistical inference and find out why.
Lesson 5
Read ArticleThe role of optimization analytics in experimentation
From ideation to execution and analysis, data and analytics can support marketers at every turn, turning any business into an insights-driven industry leader.
Lesson 6
Read ArticleWhy session-based attribution is flawed in A/B tests
To yield more actionable results, marketers should move away from session based attribution and start measuring A/B tests based on revenue per user.
Lesson 7
Read ArticleChoosing the right optimization KPI for your A/B tests
By addressing some common A/B testing pitfalls and introducing more reliable approaches to measuring the efficacy of your experiences, you can be sure to optimize for success.
Lesson 8
Read ArticleThe complex nature of running multivariate tests
How multivariate tests render impractical insights and what other solutions you can run for more optimal results.
Lesson 9
Read ArticleOutliers detection in A/B testing
Huge deviations in site behavior may be leading to unintended consequences in your A/B testing campaigns. Here’s how to reconcile them.