9781611976267 - mining imperfect data: with examples in r and python (mathematics in industry) de pearson, ronald k. (16 resultados)
Idioma: Inglés
Editorial: SIAM - Society for Industrial and Applied Mathematics 2020
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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.
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Editorial: SIAM - Society for Industrial and Applied Mathematics 2020
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Editorial: SIAM - Society for Industrial and Applied Mathematics 2020
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Idioma: Inglés
Editorial: SIAM - Society for Industrial and Applied Mathematics 2020
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Editorial: Society for Industrial & Applied Mathematics,U.S., New York 2020
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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 t…reatment 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.
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Librería: Rarewaves.com USA, London, LONDO, Reino UnidoRarewaves.com USA
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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 appropri…ate 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.
Idioma: Inglés
Editorial: Society For Industrial & Applied Mathematics,U.S. 2020
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EUR 102,07
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Paperback / Softback. Condición: Brand New. 2nd revised edition edition. 481 pages. 10.08x7.01x1.26 inches. In Stock.
Idioma: Inglés
Editorial: SIAM - Society for Industrial and Applied Mathematics 2020
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Librería: GreatBookPricesUK, Woodford Green, Reino UnidoGreatBookPricesUK
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Idioma: Inglés
Editorial: SIAM - Society for Industrial and Applied Mathematics 2020
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Librería: Books Puddle, New York, NY, Estados Unidos de AmericaBooks Puddle
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Condición: New. 2nd edition NO-PA16APR2015-KAP.
Idioma: Inglés
Editorial: SIAM - Society for Industrial and Applied Mathematics 2020
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Idioma: Inglés
Editorial: Society for Industrial & Applied Mathematics,U.S. 2020
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Paperback / softback. Condición: New. New copy - Usually dispatched within 4 working days.
Idioma: Inglés
Editorial: Society for Industrial and Applied Mathematics,U.S., US 2020
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Librería: Rarewaves.com UK, London, Reino UnidoRarewaves.com UK
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EUR 112,11
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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 appropri…ate 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.
Idioma: Inglés
Editorial: Society for Industrial & Applied Mathematics,U.S., New York 2020
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Librería: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
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EUR 172,85
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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 t…reatment 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.
