The Data Science Handbook is a very interesting book in the sense that it reminded me of another book named "Founders at Work" written by Jessica Livingston.(Link here : http://www.amazon.com/Founders-Work-Stories-Startups-Early/dp/1430210788) Both of the books posses similar formats which is to create a collection of interviews of people from respective domains which are data science and entrepreneurship.The book focuses more on the 'life-story' of some of the reputed data scientists working in different companies, either as employees or as founder of companies. The authors asked the data scientists about their early life, what motivated them to enter the industry or how they started working in the data science domain, what courses they took during undergraduate or graduate studies that helped them to enter the industry and afterwards and how they think that data science will be impacting future.What I liked most about the book is that they included stories from data scientists who transitioned from employment to entrepreneurship. I don't think there are that many people available who did that yet. Yes, there's Quid, Kaggle, Datarobot etc but I don't know many. Also, the stories were very diverse since all 25 scientists are currently focusing on different domains.For example, there's this interview from Riley Newman who's from AirBNB and Airbnb is a relatively new company, but there's also the interview from Drew Conway, who's famous for his coining of the data science ven-diagram ; Sean Gourley is well-known for his application of mathematical modelling to middle-east war, while Jace Kohlmeier switched from high frequency trading to help Khan Academy in revolutionizing education and self learning. There are also interviews of people from Facebook (company), Palantir Technologies and some more niche companies which I didn't know before because they are not completely consumer focused or their customers aren't in my age group.It's noticeable that all the people in the book talked about the importance of effective communication for a data scientist and valued the ability to ask good questions. Almost everyone also talked about the value of strong coding, visualization and experimental design skills. My personal favorite so far would be Sean Gourley because I basically liked his working style during PHD. Unlike other students who generally try to stay in a narrow problem, he followed his curiosity and started modelling war in middle east of all things.Terrorism also happens to one of my key-interests, even though I've always been interested in terrorist psychology over their team networks, so I liked his interview tremendously. He has good insights on how to transition from data science to and as I told before entrepreneurship. He also seemed to be interested in making people data-literate and focused the importance of data scientists gaining more decision-making power. I personally don't believe that storytelling or working as an evangelist is the only thing that data scientists can or should focus on in near future, getting the decision making power as founders or politicians is important and more people will eventually be interested in it, despite what people think about 'millennials' right now. So it was refreshing to know that there are other people who are thinking in my ways.I also really liked the John Foreman interview, who happens to be the chief data scientist of MailChimp, he focused on creating different metrics for user experience which might be considered 'creative' in so-many ways because they focus on the human-dimension more than the conversion rate only. For example, he talked about how they put billboards of Mailchimp logo(with a chimp in it!) to create a personal joke between the user and the company even if they don't know how many users convert after watching their logo, which is likely to be small because they didn't put their company name there.  These kind of things matter for me because I feel like that it's true that user experience design decisions can/should be heavily influenced by data but in the end human dimensions and our decision making style also influence our perception to users a lot more than we generally think. He doesn't seem to like Kaggle competitions much , while I am interested in getting better in Kaggle, but well, it's better to get different perspectives.I'm glad that this book didn't focus much on things like "What is data science? How can we define it?" style conceptual questions, mostly because I feel like that if one person can 'define' a whole growing industry in a narrow way, then that industry must not be neither big nor high growth which is not true when it comes to data science. It's true that different people had different opinions on where data science is going, but that's more or less expected.Update : I received a free copy in exchange for this review here.