9781611976267 - Mining Imperfect Data: With Examples in R and Python (Mathematics in Industry) de Pearson, Ronald K. (16 resultados)

Condición: New. 2nd edition, 481 pp., paperback, NEW!!! - If you are reading this, this item is actually (physically) in our stock and ready for shipment once ordered. We are not bookjackers. Buyer is responsible for any additional duties, taxes, or fees required by recipient's country.

Condición: new.

Condición: New.

Condición: As New. Unread book in perfect condition.

Condición: New.

Condición: New.

Paperback. Condición: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Paperback. Condición: New. Second Edition. 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.

Paperback / Softback. Condición: Brand New. 2nd revised edition edition. 481 pages. 10.08x7.01x1.26 inches. In Stock.

Condición: As New. Unread book in perfect condition.

Condición: New. 2nd edition NO-PA16APR2015-KAP.

Condición: New.

Paperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.

Paperback. Condición: New. Second Edition. 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.

Paperback. Condición: new. Paperback. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.