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

Establishing correlation and causation

The statistical measure known as correlation expresses how closely two variables are related linearly, which can be understood graphically as how close two curves overlap. It’s a typical technique for describing straightforward connections without explicitly stating cause and consequence.

The correlation matrix displays the correlation values, which quantify how closely each pair of variables is related linearly. The correlation coefficients have a range of -1 to +1. The correlation value is positive if the two variables tend to rise and fall together.

The four types of correlations that are typically measured in statistics are the Spearman correlation, Pearson correlation, Kendall rank correlation, and the point-biserial correlation.

In order for organizations to make data-driven decisions based on forecasting the result of events, correlation and regression analysis are used to foresee future outcomes. The two main advantages...