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Mining Imperfect Data

With Examples in R and Python

Second Edition

Ronald K. Pearson (author)

ISBN: 9781611976267

Publication Date: Nov 2020

Format: Paperback

Focuses on the identification and treatment of data anomalies, including examples that highlight different types of anomalies, their potential consequences if left undetected and untreated, and options for dealing with them.
£86.50

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It has been estimated that as much as 80% of the total effort in a typical data analysis project is taken up with data preparation, including reconciling and merging data from different sources, identifying and interpreting various data anomalies, and selecting and implementing appropriate treatment strategies for the anomalies that are found. This book focuses on the identification and treatment of data anomalies, including examples that highlight different types of anomalies, their potential consequences if left undetected and untreated, and options for dealing with them.

As both data sources and free, open-source data analysis software environments proliferate, more people and organizations are motivated to extract useful insights and information from data of many different kinds (e.g., numerical, categorical, and text). The book emphasizes the range of open-source tools available for identifying and treating data anomalies, mostly in R but also with several examples in Python.

Mining Imperfect Data: With Examples in R and Python, Second Edition
  • presents a unified coverage of 10 different types of data anomalies (outliers, missing data, inliers, metadata errors, misalignment errors, thin levels in categorical variables, noninformative variables, duplicated records, coarsening of numerical data, and target leakage);
  • includes an in-depth treatment of time-series outliers and simple nonlinear digital filtering strategies for dealing with them; and
  • provides a detailed introduction to several useful mathematical characteristics of important data characterizations that do not appear to be widely known among practitioners, such as functional equations and key inequalities.
Pages 481
Date Published 30 Nov 2020
Publisher SIAM - Society for Industrial and Applied Mathematics
Series Mathematics in Industry
Series Part 4
Subject/s Probability & statistics   Computer science   Maths for scientists   Maths for engineers   Mathematical modelling  
is a senior data scientist at GeoVera Holdings, a U.S. based property insurance company. He has held positions in both academia and industry and has been actively involved in both research and applications in several data-related fields, including industrial process control and monitoring, signal processing, bioinformatics, drug safety data analysis, property-casualty insurance, and software development. The author of over 100 conference and journal papers as well as six books, he is a member of SIAM and a Senior Life Member of IEEE, holds two patents, and is an author of two R packages.

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