Practical Data Science with R Book Update

April 8, 2019
By

(This article was first published on R – Win-Vector Blog, and kindly contributed to R-bloggers)

A good friend shared with us a great picture of Practical Data Science with R, 1st Edition hanging out in Cambridge at the MIT Press Bookstore.

IMG 20190404 114957

This is as good an excuse as any to share a book update.

Nina Zumel and I (John Mount) are busy revising chapters 10 and 11 of Practical Data Science with R, 2nd Edition. We expect the second edition to be able to go into production (itself a serious phase) in a couple of months.

Right now signing up for the 2nd edition Manning Early Access Program (MEAP) gets you e-access to current drafts of chapters 1 through 7 (plus hopefully soon chapters 8 and 9!) and access to all the chapters of the first edition!

We still think Practical Data Science with R, 1st Edition remains the best “how to start working as data scientist in R” book (concentrating on how to start projects, understanding real world data, preparing data for supervised machine learning, basic unsupervised learning, and how to present results). We chose R as our teaching language, as R provides a very complete “all in one” statistical and machine learning work-bench (though obviously the combination Python/Pandas/scikit-learn/Jupyter is also an impressive environment). The second edition of our book incorporates a great number of improvements including:

  • A new chapter on basic data engineering or data wrangling in R.
  • A new chapter on using our (free) vtreat package to effectively prepare data for supervised machine learning!
  • Extensively revised and improved chapters throughout.

Please check it out!

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