Best A/B Testing Courses

A/B Testing Foundations

CXL Institute

Taught by Peep Laja (CXL founder), this concise course covers the fundamentals of A/B test design. Covers the PXL prioritization framework, statistical significance, p-values, statistical power, bandit testing, and when to choose innovative vs iterative tests. A fast, focused introduction for marketers and product people who want to stop running tests that don't lead to growth.

Practical A/B Testing

LinkedIn Learning

A practical approach to A/B testing implementation covering test setup across different platforms, sample size calculations, stopping rules, and result interpretation. Includes hands-on exercises with real experiment data and guidance on common pitfalls that invalidate test results.

A/B Testing in Python

DataCamp

Focuses specifically on running and analyzing A/B tests in Python, covering hypothesis design, metric definition, and statistical testing workflows. Learners implement tests with Python tools for sampling, inference, and visualization, making it directly applicable for analysts who want to move beyond GUI-only experimentation tools.

A/B Testing Mastery

CXL Institute

Taught by Ton Wesseling (founder of Online Dialogue, 20+ years in CRO), this advanced course goes beyond surface-level tests into systematic experimentation. Covers data-driven hypothesis creation, prioritization for maximum ROI, proper result interpretation, and building a scalable testing process. Assumes at least 1,000 monthly conversions and prior testing experience.

Designing, Running, and Analyzing Experiments

Coursera

A rigorous academic approach to experiment design and analysis. Covers factorial designs, within-subjects vs between-subjects designs, counterbalancing, and statistical analysis with ANOVA. Includes practical exercises using R. Ideal for those who want deep theoretical understanding of experimental methodology.

Probability & Statistics for Machine Learning & Data Science

Coursera

Part of the Mathematics for Machine Learning and Data Science program from DeepLearning.AI. Covers probability distributions, estimation, hypothesis testing, and uncertainty quantification with a strong practical focus. Explicitly includes A/B testing, interval estimation, and Bayesian methods, making it well aligned with experimentation practitioners who also work with ML models.

Bayesian Statistics: From Concept to Data Analysis

Coursera

An introduction to Bayesian statistics with applications to A/B testing. Covers Bayes' theorem, prior and posterior distributions, credible intervals, and Bayesian decision theory. Particularly valuable for those who want to understand the Bayesian approach to experimentation used by modern tools like GrowthBook and Statsig.

Brady Neal's widely cited open course materials covering causal diagrams, identification strategies, common biases, and estimation methods. Aimed at data scientists and statisticians who want to reason about cause-and-effect beyond simple A/B tests, including observational settings. Topics such as confounding, selection bias, and adjustment strategies are directly relevant to experimentation teams working with quasi-experiments.

Experimentation Program Management

CXL Institute

Taught by Ben Labay (Managing Director at Speero), this course focuses on building experimentation as an organizational capability. Covers getting leadership buy-in, developing an experimentation operating system and common language, building prioritization frameworks, aligning teams around data-driven decisions, and proving ROI to stakeholders. For growth, marketing, and product leaders moving from isolated tests to strategic experimentation.

Optimization & Testing Mastery

DigitalMarketer

A structured certification teaching a proven 8-step optimization process for converting existing traffic into more leads and sales. Covers identifying critical pages, prioritizing CRO campaigns, developing hypotheses, collecting user data, split testing methodology, personalization, and landing page optimization. Passing a 61-question exam earns the Certified Conversion Rate Optimization Specialist credential.

Conversion Optimization Minidegree

CXL Institute

An extensive 20+ course program covering every aspect of conversion rate optimization. Includes user research, heuristic analysis, A/B testing strategy, statistics for CRO, psychology and persuasion, and building experimentation programs. Taught by industry veterans from leading optimization agencies and companies.

The Complete Conversion Rate Optimization Course

Udemy

Created by Ruben de Boer (15+ years CRO, multiple Experimentation Elite awards), this bestseller-rated course walks through the entire CRO journey. Covers research methods (analytics, interviews, session recordings, behavioral psychology), turning research into actionable insights, hypothesis testing, A/B test execution, and learning from results. Over 20,000 students enrolled.

UX Research and Strategy

Coursera

Covers qualitative research methods that feed into experiment hypothesis generation. Includes user interviews, usability testing, survey design, and journey mapping. Essential for CRO practitioners who want to move beyond random testing to strategic experimentation based on user insights.

Psychology of Digital Behavior

CXL Institute

Explores behavioral psychology principles that drive user decisions online. Covers cognitive biases, persuasion principles, behavioral economics, and habit formation. Helps experimenters generate better hypotheses based on psychological principles rather than guesswork.

Product Analytics Fundamentals

Amplitude

Learn to use product analytics to drive experimentation and product decisions. Covers event taxonomy design, funnel analysis, retention analysis, cohort analysis, and connecting analytics data to experiment setup. Essential for product managers and growth teams building data-informed products.

Mixpanel Product Analytics Certification

Mixpanel

Official certification program for the Mixpanel product analytics platform. Covers event tracking, funnel analysis, retention analysis, and connecting insights to experiment design. Includes best practices for instrumentation and maintaining data quality.

Data Science Specialization

Coursera

A 10-course comprehensive introduction to data science, including statistical inference, regression models, and machine learning. While broader than just experimentation, provides the statistical foundation needed to understand and implement rigorous experimental methods. Uses R for all exercises.

SQL for Data Science

Coursera

Essential SQL skills for experiment analysis. Covers querying databases, joining tables, aggregating data, and building experiment result dashboards. Includes specific examples of calculating conversion rates, revenue per user, and statistical metrics directly in SQL.

Data-Driven Decision Making

Coursera

A business-focused course on building data-driven decision processes. Covers data collection, analysis, visualization, and creating a data-driven culture. Provides important context for how experiments fit into organizational decision-making and how to champion evidence-based thinking across teams.

Optimizely Academy

Optimizely

Official training for the Optimizely experimentation platform. Covers experiment setup, audience targeting, SDK implementation, results analysis, and program management. Includes both web experimentation and feature experimentation (Full Stack) tracks with hands-on labs.

LaunchDarkly Academy

LaunchDarkly

Structured learning paths covering feature flag implementation, audience targeting, progressive rollouts, and experiment setup on a modern feature management platform. Directly relevant to engineering teams adopting feature flags as part of their experimentation workflow.