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2nd Edition - July 23, 2018
Author: Jules J. Berman
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… Read more
LIMITED OFFER
Immediately download your ebook while waiting for your print delivery. No promo code is needed.
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.
Researchers, engineers, data analysts, and data managers who need to deal with large and complex sets of data
1. Introduction2. Providing Structure to Unstructured Data3. Identification, Deidentification, and Reidentification4. Metadata, Semantics, and Triples5. Classifications and Ontologies6. Introspection7. Data Integration and Software Interoperability8. Immutability and Immortality9. Assessing the Adequacy of a Big Data Resource10. Measurement11. Indispensable Tips for Fast and Simple Big Data Analysis12. Finding the Clues in Large Collections of Data13. Using Random Numbers to Bring Your Big Data Analytic Problems Down to Size14. Special Considerations in Big Data Analysis15. Big Data Failures and How to Avoid (Some of) Them16. Legalities17. Data Sharing18. Data Reanalysis: Much More Important than Analysis19. Repurposing Big Data
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