Principles and Practice of Big Data

- Preparing, Sharing, and Analyzing Complex Information

  • Format
  • Bog, paperback
  • Engelsk

Beskrivelse

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.

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Detaljer
  • SprogEngelsk
  • Sidetal480
  • Udgivelsesdato25-07-2018
  • ISBN139780128156094
  • Forlag Academic Press Inc
  • FormatPaperback
Størrelse og vægt
  • Vægt970 g
  • coffee cup img
    10 cm
    book img
    19,1 cm
    23,4 cm

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    Metadata XML Speed Inheritance Fraud Classification Copyright Data protection Identification Infrastructure Intellectual property Nomenclature Open Source Terminology Semantics Scalability Verification Introspection Indexing Costs Parsing Errors Abstraction Immutability Patents Validity Standard Validation Data reduction Non-relational databases Machine translation Reflection Triple Small data Overconfidence Class Freedom of Information Act Data Sharing Observational Studies Seek Missing data Data objects Anonymization Multimodal Data Fair Use Interoperability Data Classification Versioning Data Assessment Bayesian analysis Accuracy Specification Reanalysis Encapsulation Pagerank Precision Unstructured Data Data security Data filtering Data organization Monte Carlo Simulations Overfitting Self-description Data Integration Estimation NoSQL Redundancy FOIA Meaning Resampling Open Access Polymorphism Namespace Tort Infringement Instances Retractions Abandoned data and Madey v. Duke Analysis pitfalls Benefits Big Data failures Bigness bias Approach to data analysis Big Data Definition ASCII editor class hierarchy Combining data Complexity barrier Autocoding Class object Data Ownership Data peaks Comprehensive data Curse of dimensionality Data encumbrances data values deidentification Fast correlation methods Fixing data errors frequency distributions Data flattening Data hoarding data properties Data Quality Act Data trends Data repurposing data uniqueness integration of information systems identifier legacy data long-term efforts mapping between standards limited data use agreements Full access to data Misinterpretation of results Gaussian Copula Function Hash Collision Permutating ontologic competence Random access Random character string Pseudorandom number generators Retrospective data Reidentification Serious Big Data Representative data subclass Usage agreements Superclass Variable reduction National Patient Identifier one-way hash

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