Preface

Data analytics and data science have garnered a lot of attention from businesses around the world. The amount of data generated these days is mind-boggling, and it keeps growing everyday; with the proliferation of mobiles, access to Facebook, YouTube, Netflix, or other 4K video content providers, and increasing reliance on cloud computing, we can only expect this to increase.

The task of a data scientist is to clean, transform, and analyze the data in order to provide the business with insights about its customers and/or competitors, monitor the health of the services provided by the company, or automatically present recommendations to drive more opportunities for cross-selling (among many others).

In this book, you will learn how to read, write, clean, and transform the data—the tasks that are the most time-consuming but also the most critical. We will then present you with a broad array of tools and techniques that any data scientist should master, ranging from classification, clustering, or regression, through graph theory and time-series analysis, to discrete choice modeling and simulations. In each chapter, we will present an array of detailed examples written in Python that will help you tackle virtually any problem that you might encounter in your career as a data scientist.