Principles and Practice of Big Data: Preparing, Sharing, and Analyzing Complex Information, Second Edition, updates and expands on the first edition by Jules J. Berman. This book introduces a set of techniques and algorithms tailored for big data projects, emphasizing that most analyses can be conducted without specialized software or expensive hardware. The core of every algorithm is described using just a few lines of code in popular programming languages, with Python snippets provided to illustrate key points.

Through new examples and case studies across various scientific and engineering disciplines, the book demonstrates how understanding data and asking the right questions can lead to significant insights from large and complex datasets. It covers topics such as semantics, identification, de-identification, vulnerabilities, and regulatory/legal issues, providing readers with a comprehensive toolkit for handling big data.

The book is designed to assist students and professionals from all scientific backgrounds who wish to explore beyond traditional academic boundaries in their work on big data projects.