AB Testing in Python

Learn How To Define, Start, And Analyze The Results Of An A/B Test. Improve Business Performance Through A/B Testing
AB Testing in Python
File Size :
1.10 GB
Total length :
2h 57m



365 Careers


Last update




AB Testing in Python

What you’ll learn

How to use A/B tests to improve business performance
Define A/B tests
Start A/B tests
Analyze the results of A/B tests
Measure the success of A/B tests
How to define a hypothesis
Design tracking for the metrics
How to prepare for a data science interview (when you get asked about A/B tests)
How to design A/B tests for digital products
Advanced considerations when you run multiple A/B tests at the same time

AB Testing in Python


No experience in A/B testing is required
Knowledge of basic statistics
You do not need advanced statistical knowledge
Programming abilities are not required (but for some examples we will use Python)


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 ownAlong 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 buildingYou 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!


Section 1: Introduction: Welcome to the course on A/B testing

Lecture 1 Welcome to the course!

Lecture 2 What does the course cover and what is A/B testing

Lecture 3 What are the key characteristics of an A/B test?

Lecture 4 How A/B tests are created? Who takes part?

Section 2: Defining KPIs and metrics. Practical example: Kittengram

Lecture 5 How to measure success

Lecture 6 Calculation of metrics for Kittengram – Practical example

Section 3: Setting up and executing A/B tests in practice

Lecture 7 Data instrumentation and tracking

Lecture 8 Calculating metrics

Lecture 9 Designing the experiment

Lecture 10 Set up the A/B test

Lecture 11 Statistical significance

Lecture 12 Calculate the sample size of an A/B test

Lecture 13 Example of significance power calculator

Lecture 14 A/B test – start & analysis

Lecture 15 Presenting results

Lecture 16 A/B test analysis process

Lecture 17 Compare the activity between the groups

Section 4: Advanced considerations

Lecture 18 Advanced A/B testing considerations

Lecture 19 Important ethical considerations: Privacy

Section 5: Advanced questions for interview preparation

Lecture 20 Introduction

Lecture 21 Question 1

Lecture 22 Question 2

Lecture 23 Question 3

Lecture 24 Question 4

Lecture 25 Question 5

Lecture 26 How to prepare for the interview

Section 6: Conclusion

Lecture 27 Final recommendations

Junior data scientists with no experience in A/B testing,Data science students with no working experience,Software developers,Product managers

Course Information:

Udemy | English | 2h 57m | 1.10 GB
Created by: 365 Careers

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