Exploratory Data Analysis Using by Pearson Ronald (50 results)
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
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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
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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
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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
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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.
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
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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
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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
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Published by Chapman and Hall/CRC 2020
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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
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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
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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 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
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Language: English
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
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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
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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
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Published by CRC Press 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
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Condition: New. Ronald K. Pearson currently works for GeoVera, a property insurance company in Fairfield, California, primarily in the analysis of text data. He holds a PhD in Electrical Engineering and Computer Science from the Massachussetts Institute.
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 our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
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Published by Taylor & Francis Ltd Jun 2020 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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Taschenbuch. Condition: Neu. Neuware - 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.
- Hardcover
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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
- Hardcover
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Condition: New. Ronald K. Pearson holds a PhD in Electrical Engineering and Computer Science from the Massachussetts Institute of Technology and has more than 40 years professional experience in exploratory data analysis. Dr. Pearson has held industrial, business.
Language: English
Published by CRC Press 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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