Statistical Data Cleaning with Applications in R
eBook - ePub

Statistical Data Cleaning with Applications in R

  1. English
  2. ePUB (mobile friendly)
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eBook - ePub

Statistical Data Cleaning with Applications in R

Book details
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About This Book

A comprehensive guide to automated statistical data cleaning

The production of clean data is a complex and time-consuming process that requires both technical know-how and statistical expertise. Statistical Data Cleaning brings together a wide range of techniques for cleaning textual, numeric or categorical data. This book examines technical data cleaning methods relating to data representation and data structure. A prominent role is given to statistical data validation, data cleaning based on predefined restrictions, and data cleaning strategy.

Key features:

  • Focuses on the automation of data cleaning methods, including both theory and applications written in R.
    • Enables the reader to design data cleaning processes for either one-off analytical purposes or for setting up production systems that clean data on a regular basis.
    • Explores statistical techniques for solving issues such as incompleteness, contradictions and outliers, integration of data cleaning components and quality monitoring.
    • Supported by an accompanying website featuringdata and R code.

This book enables data scientists and statistical analysts working with data to deepen their understanding of data cleaning as well as to upgrade their practical data cleaning skills. It can also be used as material for a course in data cleaning and analyses.

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Yes, you can access Statistical Data Cleaning with Applications in R by Mark van der Loo, Edwin de Jonge in PDF and/or ePUB format, as well as other popular books in Computer Science & Data Mining. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Wiley
Year
2018
ISBN
9781118897133
Edition
1
Topic
Computer Science
Subtopic
Data Mining
Index
Computer Science

Table of contents

  1. Cover
  2. Title Page
  3. Copyright
  4. Table of Contents
  5. Foreword
  6. About the Companion Website
  7. Chapter 1: Data Cleaning
  8. Chapter 2: A Brief Introduction to R
  9. Chapter 3: Technical Representation of Data
  10. Chapter 4: Data Structure
  11. Chapter 5: Cleaning Text Data
  12. Chapter 6: Data Validation
  13. Chapter 7: Localizing Errors in Data Records
  14. Chapter 8: Rule Set Maintenance and Simplification
  15. Chapter 9: Methods Based on Models for Domain Knowledge
  16. Chapter 10: Imputation and Adjustment
  17. Chapter 11: Example: A Small Data-Cleaning System
  18. References
  19. Index
  20. End User License Agreement