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Principles and Practice of Big Data. Edition No. 2

  • ID: 4519426
  • Book
  • July 2018
  • Region: Global
  • 480 Pages
  • Elsevier Science and Technology
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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

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

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Berman, Jules J.
Jules Berman holds two bachelor of science degrees from MIT (Mathematics, and Earth and Planetary Sciences), a PhD from Temple University, and an MD, from the University of Miami. He was a graduate researcher in the Fels Cancer Research Institute, at Temple University, and at the American Health Foundation in Valhalla, New York. His post-doctoral studies were completed at the U.S. National Institutes of Health, and his residency was completed at the George Washington University Medical Center in Washington, D.C. Dr. Berman served as Chief of Anatomic Pathology, Surgical Pathology and Cytopathology at the Veterans Administration Medical Center in Baltimore, Maryland, where he held joint appointments at the University of Maryland Medical Center and at the Johns Hopkins Medical Institutions. In 1998, he transferred to the U.S. National Institutes of Health, as a Medical Officer, and as the Program Director for Pathology Informatics in the Cancer Diagnosis Program at the National Cancer Institute. Dr. Berman is a past President of the Association for Pathology Informatics, and the 2011 recipient of the association's Lifetime Achievement Award. He is a listed author on over 200 scientific publications and has written more than a dozen books in his three areas of expertise: informatics, computer programming, and cancer biology. Dr. Berman is currently a free-lance writer.
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