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A/B Testing & Experimentation – The Science of Data-Driven Decision Making is a focused, hands-on course that equips learners with the theoretical and practical tools to design, run, and analyse controlled experiments in data-driven environments. A/B testing, one of the most powerful tools in analytics and product development, enables organisations to make evidence-based decisions rather than relying on intuition or assumptions.

This course covers the complete experimentation workflow — from hypothesis formulation and experimental design to analysis, interpretation, and implementation. Learners explore key statistical concepts that underpin experimentation, including randomisation, control groups, statistical power, confidence intervals, and p-values. Beyond the basics, the course delves into more advanced topics such as sequential testing, uplift modelling, and multi-armed bandit strategies used by modern digital platforms to optimise decisions in real time.

Through practical exercises using Python, SQL, and industry-relevant datasets, learners will gain the ability to structure and execute experiments that reveal true causal effects. The course bridges the gap between theoretical understanding and applied analytics, providing step-by-step guidance on setting up valid experiments, avoiding common pitfalls such as bias or sample contamination, and interpreting results in business contexts.

By learning to build scalable experimentation systems, you’ll understand how companies like Google, Amazon, Netflix, and LinkedIn drive innovation and improvement through continuous testing and measurement. Whether improving a website’s conversion rate, evaluating a marketing strategy, or enhancing user engagement, this course offers a scientific foundation for making decisions that create measurable impact.

Why Learn A/B Testing & Experimentation

In the age of digital transformation, experimentation has become a fundamental skill for anyone working with data, analytics, or decision-making. A/B testing serves as the empirical backbone of evidence-based strategy — allowing teams to measure what works, what doesn’t, and why. It turns every product change, marketing campaign, or pricing decision into an opportunity for learning and growth.

Through this course, learners will discover how to apply the scientific method to real-world business problems. Instead of relying on intuition, they will develop the capability to test hypotheses, measure the size and significance of effects, and translate data insights into actionable strategies. The course emphasises causal inference — understanding not just correlation but the true impact of a change on desired outcomes.

By mastering A/B testing and experimentation, learners will be able to:

• Quantify the actual effect of product, marketing, or process changes.

• Replace assumptions with statistical evidence in decision-making.

• Apply robust design principles to ensure validity and reliability of results.

• Build experimentation pipelines that enable ongoing data-driven innovation.

• Integrate testing culture within teams to promote learning and continuous improvement.

Organisations across sectors — from tech and e-commerce to healthcare and finance — depend on rigorous experimentation for optimisation and growth. This makes expertise in experimental design a core capability for data professionals and business leaders alike.

What You Will Gain

By completing this course, learners will acquire a comprehensive set of analytical and technical skills essential for designing and interpreting experiments. You will:

• Understand the fundamental principles of A/B testing and the logic of controlled experiments.

• Design effective experiments using randomisation, treatment-control frameworks, and power analysis.

• Conduct hypothesis testing to assess statistical significance and measure confidence in outcomes.

• Identify and mitigate biases, novelty effects, or selection errors that threaten experimental validity.

• Analyse experimental data using Python and SQL, and communicate results through data visualisation.

• Interpret key statistical metrics such as p-values, lift, conversion rates, and confidence intervals.

• Implement adaptive testing frameworks, including sequential analysis and multi-armed bandit algorithms.

• Apply experimentation to optimise marketing campaigns, UX design, pricing strategies, and digital products.

The course integrates practical, project-based learning through real-world applications.

Hands-on projects include:

• Designing and running an A/B test for website conversion optimisation.

• Evaluating a marketing campaign using uplift modelling and statistical inference.

• Implementing a multi-armed bandit algorithm to dynamically allocate traffic and maximise performance.

Each project is designed to simulate industry workflows, ensuring learners gain practical confidence in applying experimental techniques across varied contexts.

Who This Course Is For

This course is designed for learners and professionals who want to master the principles of experimentation and evidence-based decision-making:

• Data Scientists & Analysts who analyse experiments and interpret causal results.

• Product Managers who use data to guide product feature testing and innovation.

• Marketing Professionals optimising campaigns, channels, and messaging through experiments.

• UX Researchers & Designers validating hypotheses about user behaviour and interface changes.

• Students & Researchers interested in the intersection of statistics, data science, and decision theory.

A basic understanding of statistics and Python is helpful but not mandatory — the course progressively builds from foundational concepts to advanced methodologies. Each section is structured to provide both theoretical clarity and practical experience, allowing learners from varied backgrounds to follow along smoothly.

The course is highly relevant for industries where decision-making under uncertainty is critical — such as e-commerce, SaaS, fintech, and healthcare — but its principles can be applied to any field involving data analysis and experimentation.

Key Learning Takeaways

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

• Frame hypotheses and translate business problems into testable questions.

• Select appropriate metrics and determine sample sizes for experiments.

• Evaluate statistical significance and practical importance of results.

• Apply Bayesian and frequentist methods for experiment analysis.

• Design scalable experimentation systems for continuous testing and iteration.

• Communicate results effectively to technical and non-technical stakeholders.

The course builds a deep understanding of how experimentation drives innovation, empowering professionals to embed a culture of testing, measurement, and improvement within their organisations.