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.
1. 1: Introduction - p. xxviii 2. 2: Providing Structure to Unstructured Data - p. 12 3. 3: Identification, Deidentification, and Reidentification - p. 51 4. 4: Metadata, Semantics, and Triples - p. 84 5. 5: Classifications and Ontologies - p. 95 6. 6: Introspection - p. 135 7. 7: Standards and Data Integration - p. 154 8. 8: Immutability and Immortality - p. 165 9. 9: Assessing the Adequacy of a Big Data Resource - p. 182 10. 10: Measurement - p. 206 11. 11: Indispensable Tips for Fast and Simple Big Data Analysis - p. 229 12. 12: Finding the Clues in Large Collections of Data - p. 257 13. 13: Using Random Numbers to Knock Your Big Data Analytic Problems Down to Size - p. 275 14. 14: Special Considerations in Big Data Analysis - p. 302 15. 15: Big Data Failures and How to Avoid (Some of) Them - p. 319 16. 16: Data Reanalysis: Much More Important Than Analysis - p. 349 17. 17: Repurposing Big Data - p. 362 18. 18: Data Sharing and Data Security - p. 372 19. 19: Legalities - p. 393 20. 20: Societal Issues - p. 417 21. Index - p. 445
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.
1. 1: Introduction - p. xxviii 2. 2: Providing Structure to Unstructured Data - p. 12 3. 3: Identification, Deidentification, and Reidentification - p. 51 4. 4: Metadata, Semantics, and Triples - p. 84 5. 5: Classifications and Ontologies - p. 95 6. 6: Introspection - p. 135 7. 7: Standards and Data Integration - p. 154 8. 8: Immutability and Immortality - p. 165 9. 9: Assessing the Adequacy of a Big Data Resource - p. 182 10. 10: Measurement - p. 206 11. 11: Indispensable Tips for Fast and Simple Big Data Analysis - p. 229 12. 12: Finding the Clues in Large Collections of Data - p. 257 13. 13: Using Random Numbers to Knock Your Big Data Analytic Problems Down to Size - p. 275 14. 14: Special Considerations in Big Data Analysis - p. 302 15. 15: Big Data Failures and How to Avoid (Some of) Them - p. 319 16. 16: Data Reanalysis: Much More Important Than Analysis - p. 349 17. 17: Repurposing Big Data - p. 362 18. 18: Data Sharing and Data Security - p. 372 19. 19: Legalities - p. 393 20. 20: Societal Issues - p. 417 21. Index - p. 445
