Experimental Design in Python

Experimental design best practices and robust statistical analysis techniques are crucial for answering questions like "Did our new ad banner increase click-through rate?" or "Does our new product feature increase user ratings?" This course is intended for those with foundational knowledge of sampling and hypothesis testing, who want to take the next step to apply these concepts to real-world problems.

In any form of analytics, data is explored, cleaned, and prepared for analysis. To conduct an analysis that produces a precise conclusion, you'll need a set of robust statistical tools, such as those taught in this course. In this course, you'll learn how to choose the most appropriate statistical test for your use case, verifying underlying test assumptions, and how to perform a power analysis.

This course primarily uses the SciPy library for performing statistical tests, but several functions are also used from statsmodels.

Good experimental design practices are crucial in many industries. This course illustrates this by using several datasets, including marketing click-through rates (CTR), crop yields, loan approval rates, athletic performance improvements, chemical reaction rates, and more!