Book Image

The Art of Data-Driven Business

By : Alan Bernardo Palacio
Book Image

The Art of Data-Driven Business

By: Alan Bernardo Palacio

Overview of this book

One of the most valuable contributions of data science is toward helping businesses make the right decisions. Understanding this complicated confluence of two disparate worlds, as well as a fiercely competitive market, calls for all the guidance you can get. The Art of Data-Driven Business is your invaluable guide to gaining a business-driven perspective, as well as leveraging the power of machine learning (ML) to guide decision-making in your business. This book provides a common ground of discussion for several profiles within a company. You’ll begin by looking at how to use Python and its many libraries for machine learning. Experienced data scientists may want to skip this short introduction, but you’ll soon get to the meat of the book and explore the many and varied ways ML with Python can be applied to the domain of business decisions through real-world business problems that you can tackle by yourself. As you advance, you’ll gain practical insights into the value that ML can provide to your business, as well as the technical ability to apply a wide variety of tried-and-tested ML methods. By the end of this Python book, you’ll have learned the value of basing your business decisions on data-driven methodologies and have developed the Python skills needed to apply what you’ve learned in the real world.
Table of Contents (17 chapters)
Part 1: Data Analytics and Forecasting with Python
Part 2: Market and Customer Insights
Part 3: Operation and Pricing Optimization

Validating the effect of changes with the t-test

When measuring the effects of certain actions applied to a given population of users, we need to validate that these actions have actually affected the target groups in a significant manner. To be able to do this, we can use the t-test.

A t-test is a statistical test that is used to compare the means of two groups to ascertain whether a method or treatment has an impact on the population of interest or whether two groups differ from one another; it is frequently employed in hypothesis testing.

When the datasets in the two groups don’t relate to identical values, separate t-test samples are chosen independently of one another. They might consist of two groups of randomly selected, unrelated patients to study the effects of a medication, for example. While the other group receives the prescribed treatment, one of the groups serves as the control group and is given a placebo. This results in two separate sample sets that are...