A/B testing: The art and science of data-driven choices
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Master the art and science of A/B testing for data-driven decision making. Learn how to design, implement, and analyze A/B tests to optimize user experience, increase conversion rates, and make informed business decisions based on empirical data.
At a Glance
A/B testing is an essential task in the tool kits of tech giants such as OpenAI, Amazon, Google, and Netflix. It plays a crucial role in refining marketing approaches and enhancing user experiences. Dive into this captivating realm of data-driven decision-making with this A/B testing Guided Project. Is your company seeking to boost user satisfaction by launching a dark mode feature on your website? Then, seize this opportunity to master the art of data-driven choices, and discover which options work best for you.
Imagine that your company is considering the introduction of a dark mode feature on its website, with the goal of enhancing user experiences and potentially increasing conversions. Learn how to analyze data from A/B tests, analyze results using the p-value, and gain statistical intuition by using Python in this beginner-level project.
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To begin, you explore A/B testing fundamentals, learning how this powerful technique enables businesses to compare and optimize different versions of a variable. You’ll examine key metrics and formulate hypotheses to guide your experimentation.
The project continues with hands-on data manipulation using the pandas library, allowing you to clean, explore, and prepare the data set for analysis. Navigating through NumPy, you’ll engage in statistical analysis, unraveling patterns and trends in user behaviour.
The heart of the project lies in hypothesis testing with statsmodels, where you’ll assess whether the introduction of dark mode has a significant impact on website conversions.
A look at the project ahead
- A/B testing fundamentals: Understand the core principles of A/B testing and its application in optimizing digital experiences.
- Data manipulation: Learn to wrangle and analyze data efficiently using the pandas and NumPy libraries.
- Hypothesis testing: Explore hypothesis testing techniques with the statsmodels library, enabling you to make informed decisions based on data.
What you’ll need
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