Mining Imperfect Data: With Examples in R and Python - Brossura

Ronald K. Pearson (author)

 
9781611976267: Mining Imperfect Data: With Examples in R and Python
Brossura
ISBN 10: 161197626X ISBN 13: 9781611976267
Casa editrice: SIAM - Society for Industrial and Applied Mathematics, 2020

Sinossi

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.

Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.

Editore
SIAM - Society for Industrial and Applied Mathematics
Data di pubblicazione
2020
Lingua
Inglese
ISBN 10 
161197626X
ISBN 13 
9781611976267
Rilegatura
Copertina flessibile
Numero edizione
2
Numero di pagine
481
Contatto del produttore
non disponibile
Persona responsabile
non disponibile