FeaturedUdemy
A/B Testing in Python
4.8(364)12k studentsINTERMEDIATEEnglish
Korshub's take
The Korshub Take
This intermediate Python course offers a practical, hands-on dive into the fundamental world of A/B testing, a cornerstone technique used widely across search engines, e-commerce, and marketing. Rather than simply covering the basic theory, it emphasizes real-world application to optimize customer experiences and inform critical business choices.
Who It's For
This is designed for data analysts, data scientists, and engineers who want to confidently design and execute experiments, validate their hypotheses, and drive product optimization using Python.
Key Takeaways
- Experiment Design: Learn how to properly define the right metrics and estimate necessary sample sizes and test durations for conclusive results.
- Python Tooling: Gain practical experience leveraging essential Python statistical packages, including statsmodels, scipy, and pingouin.
- Analysis & Interpretation: Master the nuances of p-values and learn to analyze test results effectively, ensuring accurate conclusions that guide high-level business decisions.
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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