DataCampA/B Testing in Python
4.8(364)12k studentsINTERMEDIATEEnglish
Korshub's take
Master Data-Driven Decisions
In a digital landscape shaped by constant experimentation, the ability to interpret A/B tests is a vital skill for data professionals. This intermediate-level course moves beyond theory, teaching you how to design, execute, and evaluate experiments using Python.
What You'll Learn
- Strategic Design: Learn to define critical metrics and calculate the necessary sample sizes and durations for conclusive results.
- Python Implementation: Leverage industry-standard libraries including statsmodels, scipy, and pingouin to automate and refine your analysis.
- Statistical Mastery: Gain confidence in interpreting p-values and running validation checks to ensure your findings are accurate.
This course is ideal for data analysts and scientists looking to turn raw experimental data into actionable business intelligence that optimizes user experience and drives profit.
About this course
In this course, you will dive into the world of A/B testing, gain a deep understanding of the practical use cases, and learn to design, run, and analyze these A/B tests in Python.
Did you know that you are almost guaranteed to participate in an A/B test every time you browse the internet? From search engines and e-commerce sites to social networks and marketing campaigns — all businesses hire the best data analysts, scientists, and engineers to leverage the power of AB testing. Testing different variants can help optimize the customer experience, maximize profits, inform the next best design, and much more.
You’ll start by learning how to define the right metrics before learning how to estimate the appropriate sample size and duration to yield conclusive results. Throughout this course, you’ll use a range of Python packages to help with A/B testing, including statsmodels, scipy, and pingouin.
By the end of the course, you will be able to run the necessary checks that guarantee accurate results, master the art of p-values, and analyze the results of A/B tests with ease and confidence to guide the most critical business decisions.
Discover How A/B Tests Work
Did you know that you are almost guaranteed to participate in an A/B test every time you browse the internet? From search engines and e-commerce sites to social networks and marketing campaigns — all businesses hire the best data analysts, scientists, and engineers to leverage the power of AB testing. Testing different variants can help optimize the customer experience, maximize profits, inform the next best design, and much more.
Learn About A/B Testing in Python
You’ll start by learning how to define the right metrics before learning how to estimate the appropriate sample size and duration to yield conclusive results. Throughout this course, you’ll use a range of Python packages to help with A/B testing, including statsmodels, scipy, and pingouin.
By the end of the course, you will be able to run the necessary checks that guarantee accurate results, master the art of p-values, and analyze the results of A/B tests with ease and confidence to guide the most critical business decisions.
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