
Principles and Practice of Big Data
Preparing, Sharing, and Analyzing Complex Information
Description
Key Features
- 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
Readership
Researchers, engineers, data analysts, and data managers who need to deal with large and complex sets of data
Table of Contents
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
Product details
- No. of pages: 480
- Language: English
- Copyright: © Academic Press 2018
- Published: July 23, 2018
- Imprint: Academic Press
- eBook ISBN: 9780128156100
- Paperback ISBN: 9780128156094
About the Author
Jules Berman

Affiliations and Expertise
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