By the end of this course, learners will be able to:

1. Explain the concepts and goals of A/B testing and randomized experiments.
2. Design controlled experiments with clear hypotheses and success metrics.
3. Apply statistical methods for hypothesis testing and significance evaluation.
4. Understand randomization, control groups, and sampling strategies.
5. Compute confidence intervals and p-values for effect estimation.
6. Identify and mitigate sources of bias and interference.
7. Use uplift and causal models to measure incremental impact.
8. Conduct multi-variant and multi-armed bandit experiments.
9. Apply Bayesian and sequential testing approaches for adaptive experimentation.
10. Interpret and communicate experiment results for business impact.

Course Syllabus

Module 1: Introduction to Experimentation and A/B Testing
The role of experimentation in data-driven decision-making; key terminology and examples.

Module 2: Experimental Design Fundamentals
Defining hypotheses, treatment and control groups, and randomization techniques.

Module 3: Statistical Foundations for A/B Testing
Probability, hypothesis testing, p-values, confidence intervals, and Type I/II errors.

Module 4: Power and Sample Size Calculations
Determining the right sample size to ensure reliable test results.

Module 5: Running and Managing Experiments
Implementing tests in web, mobile, and product environments; tracking data quality.

Module 6: Interpreting and Visualizing Experiment Results
Data analysis using Python, SQL, and visualization libraries.

Module 7: Dealing with Bias, Interference, and Novelty Effects
Identifying and mitigating experimental pitfalls and external factors.

Module 8: Beyond A/B Testing – Multivariate and Multi-Armed Bandits
Exploring adaptive experimentation and optimization techniques.

Module 9: Causal Inference in Experimentation
Understanding treatment effects, counterfactuals, and causal modeling.

Module 10: Bayesian Approaches to Experimentation
Using Bayesian inference for dynamic and continuous experimentation.

Module 11: Experimentation at Scale
Building experimentation platforms and data pipelines for large organizations.

Module 12: Capstone Project – Design and Analyze a Real-World Experiment
Develop a complete A/B testing project, from hypothesis to insights, using real-world data.

Upon successful completion, learners will receive a Certificate of Mastery in A/B Testing & Experimentation from Uplatz.

This certification validates your ability to design, execute, and interpret data-driven experiments that improve product, marketing, and operational outcomes.

It demonstrates that you can:

• Apply statistical rigor to experimentation.
• Build and evaluate controlled tests using modern tools and methodologies.
• Translate data insights into actionable business decisions.

This credential affirms your readiness to contribute to data science, product analytics, marketing optimization, and growth strategy teams, empowering you to lead experimentation-driven innovation.

Expertise in experimentation and A/B testing opens diverse analytical and strategic career opportunities, including:

• Data Scientist (Experimentation)
• Product Analyst
• Growth Analyst
• Marketing Data Scientist
• Conversion Rate Optimization (CRO) Specialist
• Experimentation Platform Engineer

Industries such as e-commerce, SaaS, finance, media, and healthcare value professionals who can apply the scientific method to product and business decisions — making experimentation a key career skill in the modern digital economy.

1. What is A/B testing and why is it important?
   A/B testing compares two or more versions of a variable to determine which performs better using statistical analysis.
2. What is the difference between hypothesis testing and A/B testing?
   A/B testing applies hypothesis testing principles to real-world business or product scenarios.
3. What is statistical significance?
   It measures the likelihood that observed differences are not due to random chance, typically evaluated using p-values.
4. What is a control group?
   The group in an experiment that does not receive the treatment, used as a baseline for comparison.
5. What is a Type I and Type II error?
   Type I: false positive (rejecting a true null hypothesis).
   Type II: false negative (failing to reject a false null hypothesis).
6. What is sample size determination and why is it important?
   It ensures sufficient statistical power to detect meaningful effects.
7. How do multi-armed bandit algorithms differ from A/B tests?
   Bandits allocate traffic dynamically to the best-performing variant rather than splitting evenly.
8. What is uplift modeling?
   A technique that estimates the incremental effect of a treatment compared to control.
9. When should Bayesian testing be preferred over traditional A/B testing?
   When continuous updating, adaptive decision-making, or small samples are required.
10. What challenges arise in running real-world experiments?
    Bias, insufficient sample size, external interference, seasonality, and user overlap.