It is the most difficult to digest and comprehend book to date out of all I have recently read. At the same time it was pure fun.
Why so hard, I guess I need to blame myself because this book unexpectedly turned out to be more from the Academia world where my skills in Algebra and Statistics faded out over time than from the practical world where I spend my productive time. At the same time it was pleasant to have a feeling of being a student again.
Nevertheless, the book offers a ton of insight, and how-to’s for the in “the trenches” practitioners. This book is full of external reference and facts, it sure took a while for the authors to assemble it.
From my observations, the knowledge of the R language is necessary before starting reading this book, sadly, even if a program code is provided in the book there is no sample output.
The book is written so it has chapters by guest authors, this makes sense as a data project is rarely comprised of one kind of a professional, this nuance is also covered in the book by the way.
These guest authors are top notch professionals that would write a complete book on their own subject matter of expertise. But because they are the “top guns” in their corresponding field each managed to cover a lot of grounds just within a dedicated single chapter.
So, in short, the best thing about this book is that in one single investment you get a comprehensive coverage for life on what approach or algorithm to use against a given data science task at hand. You must feel more secure after reading this book and as a result be more eager and ready to embark on any data science project.
This book can also be used to expand your knowledge further, in many directions.
Five out of five stars.
Disclaimer: I received this book for free as part of O’Reilly Blogger Review program.