Data Science in R - Nolan, Deborah
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This book explains the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts approach a problem and reason about different ways of implementing solutions. The book's collection of projects, exercises, and sample solutions encompass practical topics pertaining to data processing and analysis. The book can be used for self-study or as supplementary reading in a statistical computing course, allowing students to gain valuable data science skills.

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Produktbeschreibung
This book explains the details involved in solving real computational problems encountered in data analysis. It reveals the dynamic and iterative process by which data analysts approach a problem and reason about different ways of implementing solutions. The book's collection of projects, exercises, and sample solutions encompass practical topics pertaining to data processing and analysis. The book can be used for self-study or as supplementary reading in a statistical computing course, allowing students to gain valuable data science skills.
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Autorenporträt
Deborah Nolan holds the Zaffaroni Family Chair in Undergraduate Education at the University of California, Berkeley. She is a fellow of the American Statistical Association and the Institute of Mathematical Statistics. Her research has involved the empirical process, high-dimensional modeling, and, more recently, technology in education and reproducible research. Duncan Temple Lang is the director of the Data Science Initiative at the University of California, Davis. He has been involved in the development of R and S for 20 years and has developed over 100 R packages. His research focuses on statistical computing, data technologies, meta-computing, reproducibility, and visualization.