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

Loading next page...
 
/lp/springer_journal/benjamin-s-baumer-daniel-t-kaplan-nicholas-j-horton-2017-modern-data-T1SXeB3Mc6
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

There are no references for this article.

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

Monthly Plan

  • Read unlimited articles
  • Personalized recommendations
  • No expiration
  • Print 20 pages per month
  • 20% off on PDF purchases
  • Organize your research
  • Get updates on your journals and topic searches

$49/month

Start Free Trial

14-day Free Trial

Best Deal — 39% off

Annual Plan

  • All the features of the Professional Plan, but for 39% off!
  • Billed annually
  • No expiration
  • For the normal price of 10 articles elsewhere, you get one full year of unlimited access to articles.

$588

$360/year

billed annually
Start Free Trial

14-day Free Trial