Online Course
A/B Testing in Python

Name: A/B Testing in Python
Price: 36 USD

with Anastasia Kuznetsova

Master A/B Testing in Python: Drive business growth and improve user experience with data-driven decisions

4.8

862 reviews on

Start for free

7,334 students already enrolled

Skill level:

Basic

Duration:

3 hours

Lessons (3 hours)

Practice exams (22 minutes)

CPE credits:

4.5

CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of professionals must earn to maintain their licenses and stay current with regulations and best practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's official website: www.nasbaregistry.org

Accredited

certificate

What you learn

Master A/B testing concepts and their technical applications.
Define, start, analyze, and measure A/B test success.
Run multiple A/B tests simultaneously to iterate faster.
Apply experiment design principles for digital products.
Excel in A/B testing interview questions and boost your career prospects.

Topics & tools

TheoryPythonA/B testingExperiment designProduct designDigital analyticsMath & StatisticsBusiness SkillsCareer Development

Your instructor

Anastasia Kuznetsova

Principal Product Analyst at King

Track record

Bringing real-world expertise from leading global companies

Academic background

Master's degree, Computer Science

Media and recognition

Toptal STEM Scholarship for Women

Ask Anastasia a question

Course overview

Accredited certificates

Course OVERVIEW

Description

CPE Credits: 4.5 Field of Study: Information Technology
Delivery Method: QAS Self Study

The A/B Testing in Python course gives you a rare opportunity to learn a skill that’s invaluable to all data scientists. Your instructor, Anastasia Kuznetsova, is a proven expert who works in one of the largest music streaming platforms in the world. In this course, she touches on several practical examples that not only allow you to learn the mechanics of A/B testing but also reinforce your understanding of how these concepts are applied in a real-life working environment.

Prerequisites

Python (version 3.8 or later), pandas library, and a code editor or IDE (e.g., Jupyter Notebook, Spyder, or VS Code)
Basic familiarity with Python programming is required.
Familiarity with basic statistics and linear algebra is helpful but not mandatory.

Advanced preparation

Curriculum

27 lessons 3 exams

1. Introduction to A/B testing

31 min

The first section of the course answers some fundamental questions such as what is A/B testing, which are the key characteristics of an A/B test, how to create an A/B test, and who takes part in the process.

31 min

The first section of the course answers some fundamental questions such as what is A/B testing, which are the key characteristics of an A/B test, how to create an A/B test, and who takes part in the process.

Welcome to the course: meet your instructor Free

What is A/B testing and why is it so important? Free

The key characteristics of an A/B test Free

How to create an A/B test? Who does it? Free

How to know if an A/B test was successful? Defining KPIs and metrics Free

Calculation of metrics in practice: Kittengram Free
2. Setting up and executing A/B tests in practice

101 min

In this part of the course, we will go through a complete practical example that shows you how a fictional company called Kittengram can benefit of A/B testing and which are some of the metrics they will use to monitor business performance.

101 min

In this part of the course, we will go through a complete practical example that shows you how a fictional company called Kittengram can benefit of A/B testing and which are some of the metrics they will use to monitor business performance.

Data instrumentation and tracking

How to calculate metrics from raw datasets

Designing the experiment

How to set up the A/B test

What is statistical significance?

Calculating the sample size of an A/B test

Example of significance power calculator

A/B test - start & analysis

How to present the results of an A/B test

A/B test analysis process

Comparing the activity between the groups

Practice exam
3. Advanced A/B testing considerations and interview prep

16 min

This section covers some important considerations on advanced A/B testing situations such as how to run multiple A/B tests simultaneously and how to ensure your A/B test is ethical from a user privacy perspective.

16 min

This section covers some important considerations on advanced A/B testing situations such as how to run multiple A/B tests simultaneously and how to ensure your A/B test is ethical from a user privacy perspective.

Advanced A/B testing considerations

How to A/B test ethically
4. Interview preparation

25 min

The course instructor will share with you how to pass successfully the A/B test part of the data science interview. This section will prove invaluable to those of you who are looking for their first job in the field of data science.

25 min

The course instructor will share with you how to pass successfully the A/B test part of the data science interview. This section will prove invaluable to those of you who are looking for their first job in the field of data science.

Introduction

Question 1

Question 2

Question 3

Question 4

Question 5

How to prepare for the interview

Practice exam
5. Conclusion

4 min

A final lecture to congratulate you on the progress you have made and recommend the next steps on your journey to become a data scientist.

4 min

A final lecture to congratulate you on the progress you have made and recommend the next steps on your journey to become a data scientist.

Conclusion
6. Course exam

30 min

30 min

Course exam

Free lessons

1.1 Welcome to the course: meet your instructor

4 min

1.2 What is A/B testing and why is it so important?

9 min

1.3 The key characteristics of an A/B test

4 min

1.4 How to create an A/B test? Who does it?

3 min

1.5 How to know if an A/B test was successful? Defining KPIs and metrics

8 min

1.6 Calculation of metrics in practice: Kittengram

3 min

Start for free

I wanted to move from IT support into solving business problems with data, but I needed structured learning and real-world practice. 365 Data Science gave me exactly that—an end-to-end path from Python to machine learning, with hands-on projects that built my confidence. Today, I analyze large datasets, build predictive models, and deliver data-driven insights as a Data Scientist. For IT professionals aiming to transition into data roles, 365 is a career-changing resource.

Basilio C.

Before 365: IT Assistant | UNDP Guinea-Bissau

After 365: Data Scientist | Orange Bissau

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96%

of our students recommend

365 Data Science.

I began my self-training with 365 Data Science before joining a Data Science Fellowship, and it played a crucial role in advancing my skills as a Data Analyst. The courses are well-organized, practical, and detailed. Now, managing AI projects while analyzing data, I often return to 365 to learn new, job-relevant skills—like Power BI, Web Scraping, and Power Query. 365 has been an ongoing resource for my professional growth.

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$29,000

average salary increase

after moving to an AI and data science career

I completed all three Data Career Tracks and the Investment Analyst Track on 365 Data Science to build a career where data meets finance. The structured learning, well-designed courses, and hands-on practice gave me the confidence to make that pivot—and it paid off. For nearly three years, I’ve applied the skills I gained to solve business problems and support data-driven decisions in the finance industry. 365 played a big role in turning that vision into reality.