It's an excellent, even mandatory book for your Data Science shelf.  I am glad I bought it.  I am 67% of the way through reading this book.  It has nowhere near enough material on some areas, though, and is just missing some material that you need for DS.  That's actually OK because of course no single book is enough to cover everything you need to know in a field.  Look how many books you may have bought just to get an undergrad degree, and I bet it was not just one book.So here is a list of good and bad about this excellent book.Its good points:The profit curve.  After reading this book, I will never use Accuracy to select a model any more, as that's nearly a worthless metric especially when there are marginal costs and marginal profits involved in an application scenario.  The book is just amazingly good on describing how to select models based on estimated profit, and foremost the profit curve, and selected other supporting curves like ROC area under curve.The expected profit computation and the cost-benefit matrix as a partner to the confusion matrix. This is great stuff.  It's not even described in other data science courses that I have taken.Other good points:  ...And don't worry about the other good points (there are some). The profit curve analysis, and the lead-up to that, are superior.Its bad points:p.224: "We will train on the complete dataset and then test on the same dataset we trained on."  What follows next the rest of the chapter is just an inappropriate error analysis, because it is overly optimistic (but otherwise the techniques are great.)  The models have seen the training data.  We should never completely assess (test) -- and base the entire remainder of the chapter material -- on error (accuracy) estimates produced from data that the models have already seen.In most chapters, there is just not enough detail in the material, to enable this book to be used as a "correct reference" basis against which to write your own working code as you follow along with the text in whatever computer language you want to use for analysis.In summary:The book is outstanding.  It is necessary for your DS bookshelf, but on the other hand it is nowhere near sufficient.The data science course sequence by Johns Hopkins University identifies many of the elements of a nice overall outline as to what DS practitioners need to be able to do (and this is not even sufficient either):Reproducible research; Experimental design; R programming (or python, or perhaps SAS or Octave, but some mathy language for sure); Exploratory data analysis; Regression models; Statistical inference; Practical machine learning; Scientific writing; Developing data products; Big data techniques (e.g. Apache Spark programming or at least MapReduce-style programming); SQL and NoSQL databases; Concurrent, distributed, and parallel programming; Advanced statistics (such as multiple testing corrections).This book by Provost et al gives just a part of the necessary DS material.  However the part it provides, is essential.  I wish the biological data scientists in academia would adopt and integrate the cost-benefit matrix idea and the profit curve idea into their model selection techniques instead of just using the accuracy metric mostly.Also a data scientist could do several follow-on added-value extensions to the profit curve chapter.  You could produce Revenue curve (or Cost) since sometimes that matters more.  You could quickly find alternatives which are nearly equi-profitable to the optimal profit but which exhibit (less revenue, less cost) or (more revenue, more cost).  You could detail the model selection and profit consequences of fixed budgets.  You could further assess the implications of marginal profit analysis on the optimal quantity when the profitability ratio changes.  You could directly assess the data science solution against the best business wisdom solution and estimate what amount of profit is lost when using the old business wisdom decisions.  It's a testament to this book's strong value that you can do a lot more based on its material.Nice work.  Recommended.