
Learn to define and measure metrics for a digital product using a fictional A/B test with Kit and Gram, including engagement, revenue, and performance KPIs.
Calculate sample size and test duration for an A/B test by balancing minimum detectable effect, power, and business constraints to determine exposure and group sizes for reliable results.
Calculate statistical significance and A/B test sample sizes in Python, using alpha, beta, minimum detectable effect, and pooled proportion for binary and continuous metrics.
Define hypotheses and success metrics, assign users randomly, and monitor with predefined significance and power. Analyze results for pre-test bias and time trends, then share findings with stakeholders.
Present a mock A/B test results deck, outlining feature, hypotheses, metrics, duration, group size, and assumptions; explain metric calculations, show significance, and propose future improvements.
Explore advanced a/b testing considerations, including risk assessment, multiple concurrent tests, cross-market features, and privacy and ethics guidelines aligned with ACM and GDPR.
Explore interview scenarios for an A/B test analysis, highlighting independence violations, seasonality, and cluster-based randomized designs such as network clustering and ego cluster randomization to ensure valid rollout.
Examine how new iOS feature exposure dilutes A/B test results and learn to assign users by OS version to measure non-dilutive impact.
Discover strategies for when you cannot AB test, using qualitative research, surveys, and interviews to understand user behavior, and apply generalized synthetic control group and time series causal impact analysis.
A/B testing is a tool that helps companies make reliable decisions based on data.
This is one of the fundamental skills you need to land a job as a data scientist or data analyst.
Do you want to become a data scientist or a data analyst?
If you do, this is the perfect course for you!
Your instructor Anastasia is a senior data scientist working at a Stockholm-based music streaming startup. She has earned two Master's degrees in Business Intelligence and Computer Science, and grown from a recent graduate to a Senior role in just 3 years. Anastasia has performed a significant number of A/B tests for large tech companies with hundreds of millions monthly users.
By taking this course, you will learn how to:
· Define an A/B test
· Start an A/B test
· Analyse the results of an A/B test on your own
Along your learning journey Anastasia will walk you through an A/B testing process for a fictional company with a digital product. This case study unfolds throughout the course and touches on everything from the very beginning of the A/B testing process to the very end including some advanced considerations. Moreover, Anastasia takes some time to share with you her advice on how to prepare for the questions on the A/B test interview for a data scientist or data analyst position.
One strong point of differentiation from statistical textbooks and theoretical trainings is that the A/B Testing in Python course will teach you how to design A/B tests for digital products that have millions or hundreds of millions of users. It is a rare overview of the A/B testing process from a business, technical, and data analysis perspective.
This is the perfect course for you if you are:
- a data science student who wants to learn one of the fundamental skills needed on the job
- junior data scientists with no experience with A/B testing
- software developers and product managers who want to learn how to run A/B tests in their company to improve the product they are building
You will learn an invaluable skill that can transform a company’s business (and your career along the way).
So, what are you waiting for?
Click the ‘Buy now’ button and let’s begin this journey today!