About the book:Data Science Handbook is a collection of in-depth interviews with 25 super smart Data Scientists who are well known for what they do. In Introduction section of the book, authors mention on which interviews will be useful to whom? Such as, aspiring Data Scientists should read interviews from Will, Clare and Diane. I would argue that irrespective of where you are in your career, all the interviews are equally helpful and amazing. For example, as a BI Consultant, who started studying Data Science recently, interviews of Drew Conway and Bradley Voytek were absolutely fantastic because Drew's interview introduced me to term Social Scientist and awesome stuff that he does with data science knowledge to make difference.Goods:Although authors would have prepared the list of questions before approaching these super busy people, the interviews feel natural and the follow up questions seem to be structured in such a way that encouraged and in case of people like Kunal Punera excited them to speak a lot more.With every interview, you find something new which kept me interested. For example, Dj Patil talking about Text Line. I had no idea about this initiative but once I read a bit more about it, I thought it was an amazing initiative. As a guy who does not live in US and isn't surrounded by news around these sort of projects, I loved how it introduced me to these projects.Highlight section throughout the book is well used and is informative.There is a variety of viewpoints on how to approach a career. For example, DJ rubbishes 10,000 hours rule and Luis Sanchez advocates 10,000 hours rule. There are other pieces of advice which are contradictory, which I believe is one of the strengths of the book. Also reminds me of the answer Soleio's answer to Why is advice largely useless? on Quora.comRecurrent theme among all the Scientists was to just do the project and not wait till you develop full stack of knowledge. Following quote by Joe Blitzstein sums it up well.Itâ€™s a dangerous way of thinking â€” that until you know X, Y, Z and W, youâ€™re not going to be able to do data science.My Favourite Interview : Sean Gourley. Didn't know who he was before reading this interviewBads:None actually. I have never read Interview style book, so I have nothing to compare against.One thing I wish it had was a question "What are your favourite non-technical books?". I think people like myself who are reading this to become better thinker would have greatly benefited from answers to this one question. I browse around a lot to find how to think better and which books to read. Finding out their favourite non-technical book would have been a good help.For future versions, I wish it includes data scientists from other countries and not just US. I think Data scientists in developing countries such as India can add different flavor to the book. At the same time, Data scientists in countries such as Australia, may come up with ideas on how to initiate when there is no strong community around data.Overall, excellent book. I was going to pirate the book but became victim of William Chen 's experiment and paid under "Pay what you want" program. And it was totally worth it.[Disclosure: I have received a free physical copy of the book from authors]