Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton (2017): modern data science with R, Chapman and Hall/CRC, 556 pp., $$\pounds $$ £ 51.19, ISBN 978-1498724487

Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton (2017): modern data science with R,... Stat Papers (2017) 58:951–952 DOI 10.1007/s00362-017-0920-x BOOK REVIEW Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton (2017): modern data science with R, Chapman and Hall/CRC, 556 pp., £51.19, ISBN 978-1498724487 Julie Zhou Received: 24 April 2017 / Revised: 4 May 2017 / Published online: 5 June 2017 © Springer-Verlag GmbH Germany 2017 This is a well written textbook, easy to read, with lots of examples and real world applications. It has 17 chapters and 6 appendices, which are organized into four parts. Part I—Introduction to Data Science (Chaps. 1–6) covers data visualization, a grammar for graphics, data wrangling, tidy data and iteration, and professional ethics. Only little experience in computer science and statistics is required here to learn R functions and coding techniques, or for reading data sets into R, selecting subsets, defining new variables, merging data sets, constructing graphs, and presenting descriptive summary statistics. Throughout, all concepts are well defined and explained. Part II—Statistics and Modeling (Chaps. 7 – 10) explains statistical foundations, statistical learning and predictive analytics (supervised learning), unsupervised learn- ing, and simulation. Some experience in statistics is required here for linear regression, or classification and cluster analysis, or principal components. R codes and examples http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Statistical Papers Springer Journals

Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton (2017): modern data science with R, Chapman and Hall/CRC, 556 pp., $$\pounds $$ £ 51.19, ISBN 978-1498724487

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2017 by Springer-Verlag GmbH Germany
Subject
Statistics; Statistics for Business/Economics/Mathematical Finance/Insurance; Probability Theory and Stochastic Processes; Economic Theory/Quantitative Economics/Mathematical Methods; Operations Research/Decision Theory
ISSN
0932-5026
eISSN
1613-9798
D.O.I.
10.1007/s00362-017-0920-x
Publisher site
See Article on Publisher Site

Abstract

Stat Papers (2017) 58:951–952 DOI 10.1007/s00362-017-0920-x BOOK REVIEW Benjamin S. Baumer, Daniel T. Kaplan, Nicholas J. Horton (2017): modern data science with R, Chapman and Hall/CRC, 556 pp., £51.19, ISBN 978-1498724487 Julie Zhou Received: 24 April 2017 / Revised: 4 May 2017 / Published online: 5 June 2017 © Springer-Verlag GmbH Germany 2017 This is a well written textbook, easy to read, with lots of examples and real world applications. It has 17 chapters and 6 appendices, which are organized into four parts. Part I—Introduction to Data Science (Chaps. 1–6) covers data visualization, a grammar for graphics, data wrangling, tidy data and iteration, and professional ethics. Only little experience in computer science and statistics is required here to learn R functions and coding techniques, or for reading data sets into R, selecting subsets, defining new variables, merging data sets, constructing graphs, and presenting descriptive summary statistics. Throughout, all concepts are well defined and explained. Part II—Statistics and Modeling (Chaps. 7 – 10) explains statistical foundations, statistical learning and predictive analytics (supervised learning), unsupervised learn- ing, and simulation. Some experience in statistics is required here for linear regression, or classification and cluster analysis, or principal components. R codes and examples

Journal

Statistical PapersSpringer Journals

Published: Jun 5, 2017

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