Welcome to the second edition of Practical Data Science Cookbook. It was the positive feedback and usefulness that the book has found for its readers that made a second edition possible. When Packt asked me to co-author the second edition, I had a preview of some of its reviews across the web and immediately found the reasons for the popularity of the book and its little weakness. Thus, the current version retains the positives of the acceptance and removes the pain points as much as possible. The two new chapters: Chapter 10, German Credit Data Analysis and Chapter 11, Forecasting New Zealand Overseas Visitors are included to enhance the usefulness of the book.
We live in the age of data. As increasing amounts are generated each year, the need to analyze and create value from this asset is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Due to this, there will be an increasing demand for people who possess both the analytical and technical abilities to extract valuable insights from data and the business acumen to create valuable and pragmatic solutions that put these insights to use. This book provides multiple opportunities to learn how to create value from data through a variety of projects that run the spectrum of types of contemporary data science projects. Each chapter stands on its own, with step-by-step instructions that include screenshots, code snippets, and more detailed explanations where necessary and with a focus on process and practical application. The goal of this book is to introduce the data science pipeline, show you how it applies to a variety of different data science projects, and get you comfortable enough to apply it in future to projects of your own. Along the way, you'll learn different analytical and programming lessons, and the fact that you are working through an actual project while learning will help cement these concepts and facilitate your understanding of them.
