A/B Testing & Experimentation Online Courses & Certifications
A/B testing is one of the methods of conducting a marketing or product experiment that helps to identify which of two pages, funnels, or product options that are similar in content, convert users to the desired action better.
The need for A/B testing stems from the fact that when creating a product, or a landing page, we do not know exactly which version will lead to the desired behavior of the audience. A/B testing methods are built on the foundations of mathematical statistics and probability theory.
The simulator is like an internship with the world’s top product teams. But, unlike an internship, you will make key decisions that affect the product and see their consequences. In a few months, you will go from an idea to a highly demanded product ready to scale. In the process, learn to identify standard situations and problems, and choose the best methods to solve them.
The growth function of a product is often associated with either marketing and promotion channels, or generating and testing hypotheses, or experimenting and optimizing funnels. These are all important components, but focusing on each of them loses the bigger picture. The purpose of the simulator is to teach you how to make a clear connection between product growth and the leverage available to the team. In the simulator, you will explore the issue of growth in a broad sense, where marketing and product become a single system.
This learning path includes the fundamental Getting Started with Amplitude Experiment course, along with courses that cover implementing Experiment in your product and writing good test plans once you are ready to start experimenting. The path is designed to give you an overview the Amplitude Experiment workflow and help set you up for success with your first experiment.
Get started with hypothesis testing by examining a one-sample t-test and binomial tests — both used for drawing inference about a population based on a smaller sample from that population.
Significance thresholds are essential to conducting hypothesis tests, and this course will introduce everything you need to evaluate the significance of your test statistics and determine if you’ve achieved statistical significance.
In this course, you’ll learn to plan, implement, and interpret a hypothesis test in Python. Hypothesis testing is used to address questions about a population based on a subset from that population. For example, A/B testing is a framework for learning about consumer behavior based on a small sample of consumers.
Learn how to set up experiments to both address research questions and weigh the trade off between resources and errors.