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Data Quality for Analytics (Table of Contents)

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Navigation: Overview --- Table of Contents --- Part I - Data Quality for Analytics Defined --- Part II - Profiling and Improvement --- Part III - Simulation Studies --- Download Page

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Table of Contents

  • Part I – Data Quality Defined
    • Chapter 1 – Introductory Case Studies
    • Chapter 2 – Definition and Scope of Data Quality for Analytics
    • Chapter 3 – Data Availability
    • Chapter 4 – Data Quantity
    • Chapter 5 – Data Completeness
    • Chapter 6 – Data Correctness
    • Chapter 7 – Specifics for Predictive Modeling
    • Chapter 8 – Specifics of Analytics for Data Quality
    • Chapter 9 – Process Considerations for Data Quality
  • Part II - Data Quality Profiling and Improvement
    • Chapter 10 - Profiling and Imputation of Missing Values
    • Chapter 11 - Profiling and Replacement of Missing Data in Time Series
    • Chapter 12 - Data Quality Control across Tables
    • Chapter 13 - Data Quality with Analytics
    • Chapter 14 - Data Quality Profiling and Improvement with SAS®Tools
  • Part III - Simulation Studies - Consequences of Poor Data Quality
    • Chapter 15 – Introduction to the Simulation Studiea
    • Chapter 16 – Simulating the Consequences of Poor Data Quality for Predictive Modeling
    • Chapter 17 – Influence of Data Quantity and Data Availability on Model Quality in Predictive Modeling
    • Chapter 18 – Influence of Data Completeness on Model Quality in Predictive Modeling
    • Chapter 19 – Influence of Data Correctness on Model Quality in Predictive Modeling
    • Chapter 20 – Simulating the Consequences of Poor Data Quality in Time Series Forecastin
    • Chapter 21 – Consequences of Data Quantity and Data Completeness in Time Series Forecasting
    • Chapter 22 – Consequences of Random Disturbances in Time Series Data
    • Chapter 23 – Consequences of Systematic Disturbances in Time Series Data

Structure of the book

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