Complete Course on A/B Testing with Interview Guide from Udemy

Have you always wondered how companies like Google, Facebook, Amazon use experimentation and AB Testing to launch successful products?

Do you want to apply online experimentation at your start-up or your current role?

Or maybe you are interviewing for a role in big Tech and wondering how to succeed in those interviews?

With the rise of smartphones, online controlled testing has really come to the forefront. If you do a google search for Experimentation or A/B testing, you will come across thousands of blogs and articles that discuss this topic. Unfortunately, most of them are either full of inaccuracies and misinterpretation of mathematical concepts Or they are too difficult to understand. This is not surprising. A/B testing is a deep area - there are many nuances involved throughout the process from conceptualization & design all the way to implementation & analysis. This course addresses this. I have designed this course to go deep into important statistical concepts but in a way that is easy to understand using everyday examples.

In just two hours, you will learn -

What product experimentation is and how to do it right
What is AB Testing, Multivariate Testing and Multi-armed Bandit Testing
What is the relevance of statistics in AB testing
What do statistical concepts such as confidence intervals, Type 1, Type 2 errors, p-value, statistical significance and statistical power mean And how do they fit in the big picture
And how to calculate sample size and duration for a successful AB test
How to excel in AB testing interviews through real interview questions

All these concepts will be reinforced with real world examples from companies such as Amazon, AirBnb, Square and Uber. I will also provide you with templates and cheat sheets that have really helped me in my career. In 2 hours, you can master product experimentation and immediately start applying it in your job or interviews . See you in the course.

What's inside

Learning objectives

How companies like google, facebook, amazon use experimentation to launch successful products
Ace experimentation & a/b testing interviews
A/b testing, multivariate testing & multi-armed bandit testing
Hypothesis testing, including inferential statistics, significance level, type i and ii errors, p-values, statistical significance and statistical power
End to end process from hypothesis generation & design to implementation & analysis
Real world examples from amazon, airbnb, square, uber
Relevance of statistics and how each statistical concept fits in the big picture of a/b testing

Sample size calculation and test results analysis using r
Use experimentation to optimize websites and app
Sample size calculation using online calculators
Use experimentation to increase conversion on landing pages or in-app campaigns
Templates & cheatsheet to generate, prioritize and analyze a/b tests
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How companies like google, facebook, amazon use experimentation to launch successful products
Ace experimentation & a/b testing interviews
A/b testing, multivariate testing & multi-armed bandit testing
Hypothesis testing, including inferential statistics, significance level, type i and ii errors, p-values, statistical significance and statistical power
End to end process from hypothesis generation & design to implementation & analysis
Real world examples from amazon, airbnb, square, uber
Relevance of statistics and how each statistical concept fits in the big picture of a/b testing
Sample size calculation and test results analysis using r
Use experimentation to optimize websites and app
Sample size calculation using online calculators
Use experimentation to increase conversion on landing pages or in-app campaigns
Templates & cheatsheet to generate, prioritize and analyze a/b tests
Show more
Show less

Syllabus

Welcome to the course on Product Experimentation!

Overview of the course

Product Experimentation Overview

Welcome to the section!

In this tutorial we will understand at a high level what product experimentation is, we will go over 5 very interesting real world examples and we will see what we can test using product experimentation.

Quiz!

Types of Experiments

Multi-armed Bandit Testing

A/B Testing vs Multi-armed Bandit Testing

Process of Testing

Hypothesis Generation

Statistics & A/B Testing

Importance of Statistics in A/B Testing

Sampling Error

Confidence Intervals

Null Hypothesis & Statistical Significance

Type 1 & Type 2 Errors

Statistical Significance & p-value

Statistical Power

AB Testing Statistics Cheat Sheet

Sample Size & Duration

Other Considerations

Calculate Sample Size

Start to Finish Example in R and Interview Preparation Guide

Resources for this section

Decco - Part 1

Decco - Part 2

A/B Testing Interview Questions

Mock A/B Testing Interview - Robinhood

Mock A/B Testing Interview - Doordash

Course Completion

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Covers A/B testing, multivariate testing, and multi-armed bandit testing, which are essential methodologies for optimizing product performance and user experience

Includes templates and cheat sheets that can help learners generate, prioritize, and analyze A/B tests, which can be valuable resources for practical application

Explores statistical concepts like confidence intervals, p-values, and statistical power, which are crucial for interpreting A/B testing results accurately

Features real-world examples from companies like Amazon, AirBnb, Square, and Uber, which helps learners understand how A/B testing is applied in practice

Requires familiarity with R for sample size calculation and test results analysis, which may pose a barrier for learners without prior experience in statistical programming

Includes an interview guide with mock interviews from Robinhood and Doordash, which can be helpful for learners preparing for roles in the tech industry

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Complete Course on A/B Testing with Interview Guide with these activities:

Review Inferential Statistics

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Solidify your understanding of inferential statistics to better grasp the underlying principles of A/B testing.

Browse courses on Inferential Statistics

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Review key concepts like p-values and confidence intervals.
Work through practice problems on hypothesis testing.

Read 'Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing'

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Gain a deeper understanding of A/B testing methodologies and best practices.

View Trustworthy Online Controlled Experiments on Amazon

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Read the book, focusing on experimental design and analysis.
Take notes on key concepts and practical tips.

Calculate Sample Sizes

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Improve your ability to determine appropriate sample sizes for A/B tests.

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Use online calculators to determine sample sizes.
Vary the parameters and observe the effect on sample size.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Write a Blog Post on A/B Testing

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Solidify your understanding by explaining A/B testing concepts in your own words.

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Choose a specific aspect of A/B testing to focus on.
Research the topic and gather relevant information.
Write a clear and concise blog post explaining the concept.

Design an A/B Test for a Website

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Apply your knowledge by designing a complete A/B test for a real-world scenario.

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Identify a website and a specific area for improvement.
Formulate a hypothesis and design the A/B test.
Determine the required sample size and duration.
Document your design and analysis plan.

Read 'Statistics for Experimenters: Design, Innovation, and Discovery'

Show steps

Gain a deeper understanding of experimental design and statistical analysis.

View Statistics for Experimenters on Amazon

Show steps

Read the book, focusing on experimental design and analysis.
Take notes on key concepts and practical tips.

Practice A/B Testing Interview Questions

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Prepare for interviews by practicing common A/B testing questions with a peer.

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Find a partner who is also studying A/B testing.
Take turns asking and answering interview questions.
Provide feedback to each other on your answers.

Career center

Learners who complete Complete Course on A/B Testing with Interview Guide will develop knowledge and skills that may be useful to these careers:

Data Scientist

A data scientist uses statistical methods to analyze data, and this course will be particularly helpful for those who focus on experimentation and causal inference. This role involves designing and interpreting A/B tests, determining statistical significance, and understanding the impact of interventions. The course's deep dive into concepts like confidence intervals, p-values, and statistical power helps a data scientist avoid common pitfalls in experimentation. Further, the training on sample size calculations will enable this professional to ensure experiments are properly designed. The course's examination of real-world examples from companies like Amazon and Airbnb would be directly applicable to many data science projects. The techniques offered by this course are essential for a data scientist working in product development or optimization.

See salaries and explore the career path for Data Scientist

Conversion Rate Optimizer

Conversion rate optimizers rely on A/B testing to improve website or app performance, and this course directly supports a conversion rate optimizer through training in how to effectively conduct A/B testing. This role involves testing different versions of a webpage or app to find what works best. The course provides the necessary foundation in statistical concepts, sample size calculation, and test length. This will help an optimizer avoid errors and misinterpretations in their analyses. The course's discussion of real world examples will help a conversion rate optimizer in their everyday work, and the templates and cheat sheets will help streamline their experimentation workflow. This course will enhance a conversion rate optimizer's effectiveness.

See salaries and explore the career path for Conversion Rate Optimizer

Growth Hacker

A growth hacker will find the content of this course essential, as growth hacking involves rapid experimentation to drive user growth and engagement. This role requires a deep understanding of A/B testing, how to test multiple variables, and how to interpret results. Learning about hypothesis generation, test design, and statistical analysis will allow a growth hacker to launch more efficient tests. The course’s coverage of multivariate testing and multi-armed bandit testing are highly relevant for this role, since these techniques are often used to optimize various aspects of the user journey. This course provides the analytical tools that growth hackers need to make data-informed decisions to drive growth.

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Product Manager

Product managers often utilize A/B testing to make informed decisions about product features and user experience. This course provides a product manager with the necessary understanding of how to design and interpret experiments. Knowing how to calculate sample sizes and analyze test results will allow a product manager to confidently launch new products and iterate on their design. This course also covers the end to end process, from hypothesis generation to implementation and analysis, and this is very beneficial for a product manager because they are usually involved with all parts of the process. The coursework will help a product manager understand the statistical concepts behind A/B testing, leading to more effective, data-driven product decisions. The practical focus on real-world examples will help product managers relate these concepts to their everyday work.

See salaries and explore the career path for Product Manager

Web Analytics Specialist

Web analytics specialists need to understand and use A/B testing to improve website effectiveness, and this course helps a web analytics specialist to deepen their knowledge of A/B testing. This role entails analyzing website data through statistical analysis to identify areas for improvement. This course covers the conceptual framework of A/B testing and also how to interpret results, which assists a web analytics specialist in their role. The course covers statistical concepts, which are crucial for proper analysis, and also provides the knowledge necessary to ensure experiments are of sufficient duration and have the proper sample size. This course helps a web analytics specialist to become more skilled in optimizing website performance.

See salaries and explore the career path for Web Analytics Specialist

Marketing Analyst

Marketing analysts rely on data to measure campaign performance, and this course helps a marketing analyst better understand the principles of A/B testing for marketing experiments. This role involves optimizing marketing efforts and website conversions through controlled tests. By understanding concepts such as confidence intervals, type I and II errors, and statistical significance, a marketing analyst can ensure that their experiments are reliable and the conclusions they draw are valid. The course highlights sample size determination, which helps marketing analysts avoid flawed experiments. The examination of real-world examples will help a marketing analyst in their everyday work, and the provided templates and cheat sheets will help streamline their workflow. This course is particularly beneficial for marketing analysts who need to use testing for campaign optimization.

See salaries and explore the career path for Marketing Analyst

Research Scientist

A research scientist, especially in fields that involve experimentation, benefits from this course's focus on A/B testing, and this training helps build a foundation for empirical research. This role requires a mastery of experimental design and statistical analysis. This course details hypothesis formation, statistical analysis, and how to correctly interpret results. It also covers many important concepts such as type I and type II errors. The course also emphasizes practical application through real-world examples and resources. This course can help a research scientist in ensuring the rigor and validity of their research, and this is especially relevant for research that uses A/B testing.

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User Experience Researcher

User experience researchers often conduct experiments to understand user behavior and improve products. This course directly supports a user experience researcher by teaching the core tenets of A/B testing, which is a vital tool for this role. The course teaches how to generate hypotheses and design experiments that are both statistically sound and insightful. The course covers concepts like statistical significance and statistical power, and this helps ensure that a user experience researcher draws the correct conclusions from their tests. The course also provides the knowledge necessary to avoid common pitfalls in A/B testing, leading to more robust user research. This course gives user experience researchers the tools needed to make significant user-centered improvements based on solid data.

See salaries and explore the career path for User Experience Researcher

Business Analyst

A business analyst can benefit from this course, as this role requires data analysis to make recommendations that improve business outcomes. Many business analysts use experimentation to evaluate process changes and product improvements. This course provides an understanding of the end-to-end process of A/B testing, from initial concept to final analysis, which allows a business analyst to contribute to strategic decision-making. The emphasis on statistical concepts and sample size calculation helps ensure that the analysis done by a business analyst is accurate and well-supported. The resources provided in the course will help business analysts improve their testing and analysis capabilities. This course may be useful for business analysts who work with data and business processes.

See salaries and explore the career path for Business Analyst

Digital Marketing Specialist

Digital marketing specialists benefit from understanding A/B testing, and this course teaches the core elements of experimentation. This role includes designing, implementing, and analyzing marketing campaigns, and A/B testing is often used to optimize landing pages and other marketing materials. This course gives a digital marketing specialist the necessary knowledge of experimental design, statistics, and how to interpret results. The lessons on multivariate testing and multi-armed bandit testing will help a professional in this field make better use of testing for website optimization. The course also improves a digital marketing specialist’s capacity to analyze campaign performance. This course may be useful to digital marketing specialists seeking to improve their analytical skills.

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Statistician

While a statistician often has a strong background in statistics, this course offers a focused perspective on applying these concepts directly to A/B testing. Statisticians sometimes need to design and analyze experiments, and this course helps strengthen that capacity. A statistician also needs to understand the statistical underpinnings of testing, using methods like confidence intervals, hypothesis testing, and p-values. This course would assist with the application of these principles to A/B testing and experimentation, as well as other common statistical methods. This course may be useful for a statistician who needs to apply their statistical knowledge in practical, real-world scenarios such as A/B testing.

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Product Analyst

Product analysts frequently use A/B testing to evaluate product performance and user behavior, and this course can help a product analyst refine their knowledge of A/B testing. This role requires an understanding of experimentation, its design, and how to properly interpret the data. This course covers a range of topics, such as type I and type II errors, p-values, and statistical significance, and these statistical principles help a product analyst to correctly interpret test results. The course also provides real world examples, which can help the product analyst see how to conduct testing in a professional environment. This course may be useful for a product analyst who wants to enhance their capabilities in A/B testing and product optimization.

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Software Engineer

Software engineers play a key role in implementing A/B tests, and this course gives them the understanding of these tests needed to perform their roles more efficiently. This role involves implementing and troubleshooting experimentation systems. The course examines the end-to-end process of A/B testing, from hypothesis generation to implementation and analysis, which helps a software engineer understand each stage of the process. The focus on real-world examples, along with the templates and cheat sheets also provided, provides software engineers with a deeper understanding of the practical applications of A/B testing. This course may be useful for a software engineer who needs to implement A/B tests or understand their underlying requirements.

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Project Manager

Project managers often oversee A/B testing initiatives as part of broader projects, and this course gives them the specific knowledge needed for planning and oversight. This role requires an understanding of the various stages of the testing process. This includes generating a hypothesis, designing the test, and analyzing results. By understanding the statistical underpinnings of A/B testing, they can better communicate with stakeholders, allocate resources, and make informed decisions. This course may be helpful for a project manager who is responsible for projects that include A/B testing.

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Operations Analyst

Operations analysts can use A/B testing concepts to optimize processes, and this course will help an operations analyst properly perform these tests. This role includes analyzing operations to identify opportunities for improvement, and A/B testing can be used to evaluate different approaches. Learning about hypothesis generation, statistical analysis, and proper interpretation of results will enable an operations analyst to more rigorously and accurately assess the effectiveness of different solutions. The focus on practical examples and resource will help operations analysts apply the concepts in a realistic context. The course may be useful for an operations analyst who needs to evaluate different processes and identify areas for improvement.

See salaries and explore the career path for Operations Analyst

Reading list

We've selected two books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Complete Course on A/B Testing with Interview Guide.

Trustworthy Online Controlled Experiments

Save

Provides a comprehensive guide to A/B testing, covering everything from experimental design to statistical analysis and practical implementation. It is commonly used by industry professionals. It offers a deeper dive into the statistical concepts introduced in the course. Reading this book will help you understand the nuances of A/B testing and avoid common pitfalls.

Trustworthy Online Controlled Experiments

Paperback

Statistics for Experimenters

Save

Provides a comprehensive guide to the design and analysis of experiments. It classic text in the field of statistics. It offers a deeper dive into the statistical concepts introduced in the course. Reading this book will help you understand the nuances of experimental design and analysis.

Statistics for Experimenters

Paperback

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Statistics for Experimenters

Kindle Edition

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Complete Course on A/B Testing with Interview Guide

What's inside

Learning objectives

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

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