Name: Mining Imperfect Data. With Examples in R and Python (en Inglés)
Price: 128070.00 CLP
Availability: InStock
Author: Ronald K. Pearson
ISBN: 9781611976267

portada Mining Imperfect Data. With Examples in R and Python (en Inglés)

Mining Imperfect Data. With Examples in R and Python (en Inglés)

Formato

Libro Físico

Editorial

SIAM - Society for Industrial and Applied Mathematics

Autor

Ronald K. Pearson

Colección

Mathematics in Industry

Año

2020

Idioma

Inglés

N° páginas

481

Encuadernación

Tapa Blanda

ISBN13

9781611976267

Mining Imperfect Data. With Examples in R and Python (en Inglés)

Ronald K. Pearson (Autor) · SIAM - Society for Industrial and Applied Mathematics · Tapa Blanda

Libro Nuevo Importado

Envío: 20 a 24 días háb.

$ 256.140$ 128.070

-50%

Costos de importación incluídos en el precio ✅

Libro Nuevo

Quedan 5 unidades

$ 128.070

Llega entre el 26 Ago y el 01 Sep a Santiago, Región Metropolitana. Seleccionar ubicación

Agregar a lista de deseos

Reseña del libro "Mining Imperfect Data. With Examples in R and Python (en Inglés)"

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.

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 Editionpresents 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; andprovides 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.