Pearson Ronald K (92 results)
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Published by Chapman and Hall/CRC 2018
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Book 48 of 56. Book 48 of 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Softcover
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Language: English
Published by SIAM - Society for Industrial and Applied Mathematics 2020
- Softcover
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Language: English
Published by Chapman and Hall/CRC 2018
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Book 48 of 56. Book 48 of 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Softcover
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Published by Chapman and Hall/CRC 2020
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Book 48 of 56. Book 48 of 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Softcover
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Published by Chapman and Hall/CRC 2018
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Book 48 of 56. Book 48 of 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Softcover
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Language: English
Published by Taylor & Francis Inc, Portland 2018
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Book 48 of 56. Book 48 of 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Hardcover
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Hardcover. Condition: new. Hardcover. Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA) and introduces the range of "interesting" good, bad, and ugly features that can be found in data, and why it is important to find them. It also introduces the mechanics of using R to… explore and explain data.The book begins with a detailed overview of data, exploratory analysis, and R, as well as graphics in R. It then explores working with external data, linear regression models, and crafting data stories. The second part of the book focuses on developing R programs, including good programming practices and examples, working with text data, and general predictive models. The book ends with a chapter on "keeping it all together" that includes managing the R installation, managing files, documenting, and an introduction to reproducible computing.The book is designed for both advanced undergraduate, entry-level graduate students, and working professionals with little to no prior exposure to data analysis, modeling, statistics, or programming. it keeps the treatment relatively non-mathematical, even though data analysis is an inherently mathematical subject. Exercises are included at the end of most chapters, and an instructor's solution manual is available.About the Author:Ronald K. Pearson holds the position of Senior Data Scientist with GeoVera, a property insurance company in Fairfield, California, and he has previously held similar positions in a variety of application areas, including software development, drug safety data analysis, and the analysis of industrial process data. He holds a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology and has published conference and journal papers on topics ranging from nonlinear dynamic model structure selection to the problems of disguised missing data in predictive modeling. Dr. Pearson has authored or co-authored books including Exploring Data in Engineering, the Sciences, and Medicine (Oxford University Press, 2011) and Nonlinear Digital Filtering with Python. He is also the developer of the DataCamp course on base R graphics and is an author of the datarobot and GoodmanKruskal R packages available from CRAN (the Comprehensive R Archive Network). "A CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa plc." Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
- Hardcover
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Condition: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.
Language: English
Published by Chapman and Hall/CRC 2018
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Book 48 of 56. Book 48 of 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Softcover
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Published by Chapman and Hall/CRC 2020
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Book 48 of 56. Book 48 of 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Softcover
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Language: English
Published by Taylor and Francis Inc 2018
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Book 48 of 56. Book 48 of 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Softcover
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Language: English
Published by Chapman and Hall/CRC 2020
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Book 48 of 56. Book 48 of 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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Published by Chapman and Hall/CRC 2020
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Book 48 of 56. Book 48 of 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Softcover
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Published by Chapman and Hall/CRC 2018
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Book 48 of 56. Book 48 of 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Softcover
Seller: GreatBookPricesUK, Woodford Green, United KingdomGreatBookPricesUK
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Language: English
Published by Taylor & Francis Group 2018
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Book 48 of 56. Book 48 of 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Softcover
Seller: Majestic Books, Hounslow, United KingdomMajestic Books
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Condition: New. pp. 562.
Language: English
Published by Chapman and Hall/CRC 2018
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Book 48 of 56. Book 48 of 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Softcover
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Published by Taylor & Francis Group 2020
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Book 48 of 56. Book 48 of 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Softcover
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Published by Taylor & Francis Ltd 2020
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Book 48 of 56. Book 48 of 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Softcover
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Published by Taylor & Francis Group 2020
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Book 48 of 56. Book 48 of 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Softcover
Seller: Biblios, frankfurt am main, GermanyBiblios
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Condition: New. pp. 562.
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Published by Taylor & Francis Inc 2018
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Book 48 of 56. Book 48 of 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Softcover
Seller: THE SAINT BOOKSTORE, Southport, United KingdomTHE SAINT BOOKSTORE
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Published by Chapman & Hall 2018
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Book 48 of 56. Book 48 of 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Softcover
Seller: Revaluation Books, Exeter, United KingdomRevaluation Books
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Published by SIAM - Society for Industrial and Applied Mathematics 2020
- Softcover
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Published by Taylor & Francis Group 2018
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Book 48 of 56. Book 48 of 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Softcover
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Published by SIAM - Society for Industrial and Applied Mathematics 2020
- Softcover
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Published by SIAM - Society for Industrial and Applied Mathematics 2020
- Softcover
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Published by SIAM - Society for Industrial and Applied Mathematics 2020
- Softcover
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Published by Taylor & Francis Group 2018
Series: Chapman & Hall/CRC Data Mining and Knowledge Discovery, Book 48 of 56. Book 48 of 56 - Chapman & Hall/CRC Data Mining and Knowledge Discovery
- Softcover
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Published by SIAM - Society for Industrial and Applied Mathematics 2020
- Softcover
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Language: English
Published by Society for Industrial & Applied Mathematics,U.S., New York 2020
- Softcover
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Paperback. Condition: 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.
Language: English
Published by MP-SIA SIAM - Society for Industrial and Applied M 2020
- Softcover
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