Preface

Unsupervised learning is an increasingly important branch of data science, the goal of which is to train models that can learn the structure of a dataset and provide the user with helpful pieces of information about new samples. In many different business sectors (such as marketing, business intelligence, strategy, and so forth), unsupervised learning has always had a primary role in helping the manager to make the best decisions, based both on qualitative and, above all, quantitative approaches. In a world where data is becoming more and more pervasive and storage costs are dropping, the possibility of analyzing real, complex datasets is helping to transform old-fashioned business models into new, more accurate, more responsive, and more effective ones. That's why a data scientist might not have a clear idea about all the possibilities, focusing on the pros and cons of all methods and increasing their knowledge about the best potential strategies for every specific domain. This book is not intended to be an exhaustive resource (which is actually impossible to find), but more of a reference to set you off on your exploration of this world, providing you with different methods that can be immediately employed and evaluated. I hope that readers with different backgrounds will learn worthwhile things for improving their businesses, and that you'll seek more study of this fascinating topic!