Data Simplification

Data Simplification

Taming Information With Open Source Tools

1st Edition - March 9, 2016
This is the Latest Edition
  • Author: Jules Berman
  • Paperback ISBN: 9780128037812
  • eBook ISBN: 9780128038543

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Description

Data Simplification: Taming Information With Open Source Tools addresses the simple fact that modern data is too big and complex to analyze in its native form. Data simplification is the process whereby large and complex data is rendered usable. Complex data must be simplified before it can be analyzed, but the process of data simplification is anything but simple, requiring a specialized set of skills and tools. This book provides data scientists from every scientific discipline with the methods and tools to simplify their data for immediate analysis or long-term storage in a form that can be readily repurposed or integrated with other data. Drawing upon years of practical experience, and using numerous examples and use cases, Jules Berman discusses the principles, methods, and tools that must be studied and mastered to achieve data simplification, open source tools, free utilities and snippets of code that can be reused and repurposed to simplify data, natural language processing and machine translation as a tool to simplify data, and data summarization and visualization and the role they play in making data useful for the end user.

Key Features

  • Discusses data simplification principles, methods, and tools that must be studied and mastered
  • Provides open source tools, free utilities, and snippets of code that can be reused and repurposed to simplify data
  • Explains how to best utilize indexes to search, retrieve, and analyze textual data
  • Shows the data scientist how to apply ontologies, classifications, classes, properties, and instances to data using tried and true methods

Readership

Researchers in academia and graduate students in Computer Science with an interest in machine learning.

Table of Contents

  • 1. The Simple Life
    2. Structuring Text
    3. Indexing Text
    4. Understanding Your Data
    5. Identifying and Deidentifying Data
    6. Giving Meaning to Data
    7. Object-oriented data
    8. Problem simplification 

Product details

  • No. of pages: 398
  • Language: English
  • Copyright: © Morgan Kaufmann 2016
  • Published: March 9, 2016
  • Imprint: Morgan Kaufmann
  • Paperback ISBN: 9780128037812
  • eBook ISBN: 9780128038543
  • About the Author

    Jules Berman

    Jules Berman
    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 freelance writer.

    Affiliations and Expertise

    Freelance author with expertise in informatics, computer programming, and cancer biology