Principles and Practice of Big Data, 2nd Edition

Principles and Practice of Big Data: Preparing, Sharing, and Analyzing Complex Information

Principles and Practice of Big Data: Preparing, Sharing, and Analyzing Complex Information, Second Edition updates and expands on the first edition, bringing a set of techniques and algorithms that are tailored to Big Data projects. The book stresses the point that most data analyses conducted on large, complex data sets can be achieved without the use of specialized suites of software (e.g., Hadoop), and without expensive hardware (e.g., supercomputers). The core of every algorithm described in the book can be implemented in a few lines of code using just about any popular programming language (Python snippets are provided).

Through the use of new multiple examples, this edition demonstrates that if we understand our data, and if we know how to ask the right questions, we can learn a great deal from large and complex data collections. The book will assist students and professionals from all scientific backgrounds who are interested in stepping outside the traditional boundaries of their chosen academic disciplines.

• Presents new methodologies that are widely applicable to just about any project involving large and complex datasets
• Offers readers informative new case studies across a range scientific and engineering disciplines
• Provides insights into semantics, identification, de-identification, vulnerabilities and regulatory/legal issues
• Utilizes a combination of pseudocode and very short snippets of Python code to show readers how they may develop their own projects without downloading or learning new software

Table of Contents

Chapter 1. Introduction
Chapter 2. Providing Structure To Unstructured Data
Chapter 3. Identification, Deidentification, And Reidentification
Chapter 4. Metadata, Semantics, And Triples
Chapter 5. Classifications And Ontologies
Chapter 6. Introspection
Chapter 7. Data Integration And Software Interoperability
Chapter 8. Immutability And Immortality
Chapter 9. Assessing The Adequacy Of A Big Data Resource
Chapter 10. Measurement
Chapter 11. Indispensable Tips For Fast And Simple Big Data Analysis
Chapter 12. Finding The Clues In Large Collections Of Data
Chapter 13. Using Random Numbers To Bring Your Big Data Analytic Problems Down To Size
Chapter 14. Special Considerations In Big Data Analysis
Chapter 15. Big Data Failures And How To Avoid (Some Of) Them
Chapter 16. Legalities
Chapter 17. Data Sharing
Chapter 18. Data Reanalysis: Much More Important Than Analysis
Chapter 19. Repurposing Big Data