Ronald K Pearson (111 results)

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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.…

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Condition: New. 2026. 2nd Edition. paperback. . . . . .

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Paperback. Condition: New. Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA), and this revised edition is accompanied by the R package ExploreTheData that implements many of the approaches described. As before, the primary focus of the book is on identifying "interesting" features - good, bad, and ugly - in a dataset, why it is important to find them, how to treat them, and more generally, the use of R to explore and explain datasets and the analysis results derived from them.The book begins with a brief overview of exploratory data analysis using R, followed by a detailed discussion of creating various graphical data summaries in R. Then comes a thorough introduction to exploratory data analysis, and a detailed treatment of 13 data anomalies, why they are important, how to find them, and some options for addressing them. Subsequent chapters introduce the mechanics of working with external data, structured query language (SQL) for interacting with relational databases, linear regression analysis (the simplest and historically most important class of predictive models), and crafting data stories to explain our results to others. These chapters use R as an interactive data analysis platform, while Chapter 9 turns to writing programs in R, focusing on creating custom functions that can greatly simplify repetitive analysis tasks. Further chapters expand the scope to more advanced topics and techniques: special considerations for working with text data, a second look at exploratory data analysis, and more general predictive models. 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.…

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Condition: New. 2026. 2nd Edition. paperback. . . . . . Books ship from the US and Ireland.

Paperback. Condition: New. Exploratory Data Analysis Using R provides a classroom-tested introduction to exploratory data analysis (EDA), and this revised edition is accompanied by the R package ExploreTheData that implements many of the approaches described. As before, the primary focus of the book is on identifying "interesting" features - good, bad, and ugly - in a dataset, why it is important to find them, how to treat them, and more generally, the use of R to explore and explain datasets and the analysis results derived from them.The book begins with a brief overview of exploratory data analysis using R, followed by a detailed discussion of creating various graphical data summaries in R. Then comes a thorough introduction to exploratory data analysis, and a detailed treatment of 13 data anomalies, why they are important, how to find them, and some options for addressing them. Subsequent chapters introduce the mechanics of working with external data, structured query language (SQL) for interacting with relational databases, linear regression analysis (the simplest and historically most important class of predictive models), and crafting data stories to explain our results to others. These chapters use R as an interactive data analysis platform, while Chapter 9 turns to writing programs in R, focusing on creating custom functions that can greatly simplify repetitive analysis tasks. Further chapters expand the scope to more advanced topics and techniques: special considerations for working with text data, a second look at exploratory data analysis, and more general predictive models. 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.…

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