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


As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.


advanced analytics

using, in business 4

Akaike’s Information Criteria (AIC) 92

analysis of variance (ANOVA) 26, 27

Apriori algorithm

used, for performing market basket analysis 143-148

used, for product bundling 142, 143


Bayesian Information Criteria (BIC) 92

binary logistic regression 95

business operations

improving, with web analytics 235

business-to-business (B2B) 176


causality 32

causation 32-37

choice-based conjoint 81

churn 154

client segments

creating 190-196

clustering 41, 42, 193


as customer segments 196-206

conjoint analysis 80, 81

conjoint studies 80

uses 80

conjoint experiment

designing 82, 83

correlation 29

correlation heatmap 32

correlation matrix 29-31

corr method...