9781611976267 - mining imperfect data: with examples in r and python di ronald k. pearson (author) (16 risultati)
Lingua: Inglese
Editore: SIAM - Society for Industrial and Applied Mathematics 2020
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Da: Zubal-Books, Since 1961, Cleveland, OH, U.S.A.Zubal-Books, Since 1961
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EUR 45,23
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Condizione: New. *Price HAS BEEN REDUCED by 10% until Monday, June 29 (weekend SALE item)* 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.
Lingua: Inglese
Editore: SIAM - Society for Industrial and Applied Mathematics 2020
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Da: Brook Bookstore On Demand, Napoli, NA, ItaliaBrook Bookstore On Demand
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EUR 89,37
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Condizione: new.
Lingua: Inglese
Editore: SIAM - Society for Industrial and Applied Mathematics 2020
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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EUR 103,46
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Condizione: New.
Lingua: Inglese
Editore: SIAM - Society for Industrial and Applied Mathematics 2020
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Da: GreatBookPrices, Columbia, MD, U.S.A.GreatBookPrices
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EUR 104,31
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Condizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: SIAM - Society for Industrial and Applied Mathematics 2020
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Da: Kennys Bookshop and Art Galleries Ltd., Galway, GY, IrlandaKennys Bookshop and Art Galleries Ltd.
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EUR 96,85
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Condizione: New.
Lingua: Inglese
Editore: SIAM - Society for Industrial and Applied Mathematics 2020
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Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
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EUR 98,24
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Condizione: New.
Lingua: Inglese
Editore: MP-SIA SIAM - Society for Industrial and Applied M 2020
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Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
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EUR 110,36
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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
Lingua: Inglese
Editore: Society for Industrial and Applied Mathematics,U.S., US 2020
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Da: Rarewaves.com USA, London, LONDO, Regno UnitoRarewaves.com USA
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EUR 119,10
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Paperback. Condizione: 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 appropr…iate 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.
Lingua: Inglese
Editore: Society For Industrial & Applied Mathematics,U.S. 2020
- Brossura
Da: Revaluation Books, Exeter, Regno UnitoRevaluation Books
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EUR 102,05
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Paperback / Softback. Condizione: Brand New. 2nd revised edition edition. 481 pages. 10.08x7.01x1.26 inches. In Stock.
Lingua: Inglese
Editore: Society for Industrial & Applied Mathematics,U.S., New York 2020
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Da: Grand Eagle Retail, Bensenville, IL, U.S.A.Grand Eagle Retail
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 120,50
Spedizione gratuitaSpedito in U.S.A.Quantità: 1 disponibili
Paperback. Condizione: 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.
Lingua: Inglese
Editore: SIAM - Society for Industrial and Applied Mathematics 2020
- Brossura
Da: GreatBookPricesUK, Woodford Green, Regno UnitoGreatBookPricesUK
Contatta il venditoreVenditore con 5 stelleCondizione: Usato - Come nuovo
EUR 105,89
EUR 17,38 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 5 disponibili
Condizione: As New. Unread book in perfect condition.
Lingua: Inglese
Editore: SIAM - Society for Industrial and Applied Mathematics 2020
- Brossura
Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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EUR 128,23
EUR 3,50 spedizioneSpedito in U.S.A.Quantità: 3 disponibili
Condizione: New. 2nd edition NO-PA16APR2015-KAP.
Lingua: Inglese
Editore: Society for Industrial & Applied Mathematics,U.S. 2020
- Brossura
Da: THE SAINT BOOKSTORE, Southport, Regno UnitoTHE SAINT BOOKSTORE
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EUR 109,57
EUR 24,45 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 5 disponibili
Paperback / softback. Condizione: New. New copy - Usually dispatched within 4 working days.
Lingua: Inglese
Editore: SIAM - Society for Industrial and Applied Mathematics 2020
- Brossura
Da: Kennys Bookstore, Olney, MD, U.S.A.Kennys Bookstore
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EUR 125,80
EUR 9,22 spedizioneSpedito in U.S.A.Quantità: 1 disponibili
Condizione: New.
Lingua: Inglese
Editore: Society for Industrial and Applied Mathematics,U.S., US 2020
- Brossura
Da: Rarewaves.com UK, London, Regno UnitoRarewaves.com UK
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EUR 112,09
EUR 75,31 spedizioneSpedito da Regno Unito a U.S.A.Quantità: 3 disponibili
Paperback. Condizione: 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 appropr…iate 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.
Lingua: Inglese
Editore: Society for Industrial & Applied Mathematics,U.S., New York 2020
- Brossura
Da: AussieBookSeller, Truganina, VIC, AustraliaAussieBookSeller
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 171,82
EUR 32,48 spedizioneSpedito da Australia a U.S.A.Quantità: 1 disponibili
Paperback. Condizione: 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.
