I purchased and read this book because I was interested in learning about what data scientists "do" on a day-to-day basis, and the book definitely delivers in that respect.  It is very up-front in stating that it does not cover many of the details (e.g., requisite math/statistics background, coding in R, etc.), but it provides numerous references for learning this material for those who wish to take a deeper dive.  It provides perspectives from a number of experts in the field, along with their associated domains of expertise, and hence appears to give a good sampling of the different backgrounds and personalities of self-labelled data scientists.From a pedagogical perspective, I think it's a valiant attempt to create a class that's not so "academic."  The book is a compendium of individual lectures that were the basis of a data science class at Columbia University, and the corresponding assignments were aimed at giving students a flavor of real-world data science problems (where data is messy, specific questions regarding outcomes are not-well-formed, etc.), which I think is an amazingly valuable experience to give students perspective on what the field is about.  But I wonder if the course went a little *too* far into "the real world is not pretty" aspect of things.  The students wrote a chapter at the end of the book where they criticize a standard academic problem that comes from a statistical learning textbook and that, in my opinion, is a negative outcome.  While elegant mathematical theories do not describe the complexities of the real world, understanding the subtleties of algorithms is an important part of any scientific field, and to discount that is a disservice to the students.  Too much depth and not enough breadth is bad in the real world, but so is not having *enough* depth.  On the whole, though, I really applaud the approach of the authors at building a "real-world" class.One of the things that book has left me pondering is whether there is a clear distinction between data *science* and data *engineering*, and whether it is possible to have clear roles for people who are predominately scientists than those who are predominately engineers.  A few of the guest lecturers claim that 90% of the work of a data scientist is organizing the data, but I can't help but wonder if that's the bias of the individual speakers---that they *like* this component of the work and, therefore, focus on that more, rather than it being an absolute must to spend 90% of your time working on data structuring.  I am sure that if the authors were to read this comment, they would think that I am missing one of their major points (namely, that one can make a lot of mistakes in data leakage and that like from poor data structures, hence making this an integral part of the process).  But I think there's a certain personality type that really loves working on the actually processing component who would otherwise be turned off my too much data structuring if it really composed 90% of the work.  And harnessing the skills from those types of people is undoubtedly invaluable.  So I'm really wondering if many companies are able to break up roles where some people (the scientists) focus more on the processing part, while others (the engineers) focus more on the structuring part.