Product Description
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).
Chapman & Hall Exploratory Data Analysis Using R 09780367571566
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).
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