Lillian Pierson has written a remarkable book on one count (explaining Data Science) and a not so great one on another count (actually teaching the methods of Data Science).  Based on the latter the usual audience of the â€œFor Dummiesâ€ series may be somewhat disappointed.After studying this book, you will not have developed the adequate proficiency to undertake any of the quantitative, programming, or spatial visualization methods the author presents.  This is not surprising.  You wonâ€™t readily pick up the entire fields of probability, regressions, and Monte Carlo simulation in the barely 20 pages that the author assigns to this matter.  Similarly, you wonâ€™t be able to program much if anything by reading the 20 pages chapter on either Python or R.  As you know, there are entire For Dummies books written on many of those subjects separately.  And, many of those actually struggle to cover one single field adequately.  For instance, in my view the â€œR for Dummiesâ€ and â€œProbability for Dummiesâ€ are mediocre (after studying them I rated both of them a 3).  So, if others could not well cover R over 350 pages, what chance would Pierson have to cover it adequately in 20 pages.However, this is not to say that this book does not have much value.  It really does.  And, that is in defining what Data Science is, in exploring all its aspects, in differentiating between Data Engineers and Data Scientists, in surveying practical solutions of how many fields are currently using Data Science and how, and in informing you what exists out there.Additionally, some of the chapters are surprisingly strong (relative to some of the quick overview of specific subjects as demonstrated by other chapters).  This includes many chapters near the end.  Chapter 19 on Environmental Data Science is excellent.  You can tell this is one of the authorâ€™s subspecialties.  Chapter 20 on Data Science for driving E-Commerce is also excellent.  The author has a surprisingly broad and deep culture on this specific topic.  The Part of Tens is very good.  Sometimes within the Dummies Series, the Part of Tens are wasted fillers mandated by the For Dummies format.  This is not the case here, as this section is very informative with many excellent free data sources and data tools.In summary, the book is excellent in defining what Data Science is.  It is not always that good at teaching how to practice Data Science.  As indicated above, this is no fault of the author. You canâ€™t possibly teach a dozen complex mathematical disciplines, computer programs, and data visualization techniques in 350 lightly written pages.  As mentioned, it is already remarkable that in that small pace she could survey and define so well all those disciplines (and give you a rudimentary idea of how to practice those).The book can be used in many different ways.For many readers it may serve as a pretty good benchmark of where they stand.  If one is reading this book, it is not unlikely that you are already half way there to being a Data Scientist.  This book will clearly spell out what are the skills you already have that qualify you as a Data Scientist, and what skill you are still missing to be a full fledged one.This book can serve as an excellent stepping stone to further study many aspects of this fascinating field.  Pierson has a very deep and broad culture in Data Science.  She refers to tons of stuff, including very powerful open source web based data visualization and data analysis software that most people (even quants themselves) may have never heard off.  She also defines well how to use certain computer programs and what they can do for you.  Thus, if you are hesitating between learning R or Python, she will provide much valid information on the subject to facilitate your decision.  In other words, Pierson provides an excellent survey of what exists out there, and what fields, methods, or programs are out there that you could study next.  That alone is a huge value and is the main value of the book.