This is not your typical â€œdummiesâ€ book, in that it provides an introduction to a topic which any reasonably educated person can digest and understand.  You better have a couple of semesters of solid college statistics, and some basic programming skills (or at least concepts) under your belt before you dive into this.If you took statistics sometime in the past , and vaguely remember something about t tests and p values this is NOT the book for you.  If R is a letter of the alphabet and Python is a snake then stop reading right here.  Go find something else.That said, this is a lot of information to digest.  A lot.  It provides a broad overview of data science, but filled with a very large amount of jargon which is not adequately explained.  Some very simplistic concepts (cumulative probability, what constitutes and independent and dependent variable, the equation of a line as example) are outlined in detail. I seriously doubt that someone who does not know what each part of  y=mx+b stands for in the equation of a line is going to be able to handle the rest of the book.  Sort of like starting a calculus book with an explanation of how to add two digit numbers, if you donâ€™t  know how to add you are too far behind to start up with calculus.  Other concepts are explained with rather strange analogies (reaction to a stove burner (learning to avoid) is like k-nearest neighbor).  Huh?  I am a big fan of analogies, linking understood material to new material, but many of these left me scratching my head.And, after ping ponging around between  really basic stuff, advanced ideas and programming jargon we get a section on explaining data to others.  A sort of psychology of data presentation where the author literally says that data can be presented in a â€œstorytelling mannerâ€ to tell the audience what they should conclude,  or in a manner to â€œinduce a calculating, exacting responseâ€ with â€œcorporate types, engineers, scientistsâ€ or in an â€œemotionally provocativeâ€ manner in order to influence the emotions of the viewer.  The example given is a chart labeled â€œIraqâ€™s bloody tollâ€ which resembles blood dripping.  The author notes that â€œthis style of data visualization leaves a lot of room for artistic creativity and experimentationâ€  This makes me gnash my teeth.  Maybe because I would fall into those who would want a â€œcalculating exacting responseâ€, but teaching people how to manipulate data presentation to elicit a response just rubs me the wrong way.  It does not go so far as to suggest lying with statistics, but points out you can present them in an artistic manner to try to sway people one way or another.  I guess that is business, but coming from education (I teach Honors statistics at a university) this is the sort of thing I try to teach my students to avoid.The last section was actually the best.  Basically it gives examples of stats in action, such as predictive policing and growth in e-commerce.  This is followed up with some sections with useful info about open data resources.  The hints about where to find info for doing specific analysis in R scattered throughout the book are also helpful.I see a very narrow market for this book, which should not be presented as â€œData Scienceâ€ for dummies.  This is â€œData science for people who already know pretty advanced statistics and have at least some familiarity with programming and want a general overview of what more advanced techniques can accomplish â€.  I suppose that is too long of a title to fit on a book.