Modern Data Science with R (Chapman & Hall/CRC Texts in Statistical Science)

I use this book regularly in my work as an accredited statistician working in industry. I read it in the Fall of 2017. It is clear that the authors have been using these methods for a long time. I am glad they took the time to write this book.

The book was new, no folded, or written when I received it. The code was new and worked very well. The content is informative; graph, examples are well-designed.

This book is both current and highly effective. Benjamin Baumer is very successful in facilitating and enhancing the reader's understanding of data science within the context of R.

Thebook was in great shape!

this is titled modern Data science with R:: then I read the first 9 chapters..I state I have a discrete bases of data analysis and machine learning: I found These chapters, for 90%, useless...Nothing improved in data visualization methods (with ggplot2, that is a great library!), neither in R structure data manipulation; ok a functionally usage of plyr, dplyr package, but ever a" basic use"; rather more advanced features Are totally absent; all part of predictive analysis and ensemble is too elementary; online tutorial are more advanced, sorry but it's ridiculous.. ensemble? ..autors did better not to mention them..please ;) if you have some bases in data analysis with R, save your money! Look elsewhere! for example Discrete data analysis by friendly, more and more advanced topics

This book was clearly written for someone who is an aspiring "Data Scientist" with absolutely zero previous knowledge of R and sadly statistics. If you already have a background in statistics, you will find the oversimplification irritating and just plain stupid. I actually felt angry reading some parts, knowing this is entirely inadequate. Who this book is NOT for: Someone with a statistics and ML background. Someone who reads a lot of R documentation and R-bloggers. Who this book is for: Some who is jumping on the "Data Science" bandwagon and just needs some talking points. People who hate looking at math.

First things first: The book is absolutely gorgeous; hard-cover binding, glossy pages and all in full-color! Targeted audience: Beginner to intermediate, though there are a few advanced chapters. Style: The authors go for breath over depth. With 17 chapters, the authors cover a wide range of topics. I personally don’t like a few chapters, but who cares. In my view, a few chapters are 10X better than any other source or programming language. Personal highlights: - “Chapter 7: Simulation with Bootstrapping.” Hands down, the best introduction ever of bootstrapping for the real-world. It starts with picking flights to San Francisco, analyzing delays and finally bootstrapping. - “Chapter 4: Data Wrangling.” A very accessible introduction to dyplr. Ben’s experience as the first full-time statistical analyst for the New York Mets shines (a.k.a. sabermetrics). - “Chapter 5: Introduction to tidy data and iteration.” Again, a super accessible introduction to tidy data. Accessible in the sense that it’s simply not boring. Different datasets, great code sample combined with tips such as why we should avoid for loops. - “Chapter 10: Simulation” combined with “Appendix C: Algorithmic Thinking”. To me, this is the most advanced chapter, but the “real-world” samples makes it accessible. I’m tempted to conclude that if you start Data Science in R, this should be the first book to read. If you want to dig deeper, there’s of course the “bible” for Data Science “R for Data Science.” If you want to dig deeper into bootstrapping, I highly recommend “Statistical Inference via Data Science” which covers the R package infer which enables you among other things, to great stunning bootstrapping visualizations. Conclusion: “Modern Data Science with R” is a masterpiece. Franco

Everything ok