Paperback: 346 pages
Publisher: Data Science Bookshelf, The (June 19, 2015)
Product Dimensions: 6 x 0.8 x 9 inches
Shipping Weight: 12.6 ounces (View shipping rates and policies)
Average Customer Review: 4.2 out of 5 stars See all reviews (13 customer reviews)
Best Sellers Rank: #272,702 in Books (See Top 100 in Books) #146 in Books > Computers & Technology > Databases & Big Data > Data Mining
I have a somewhat different take on this book than the other two reviews posted to date. However, I've read them both, and they're legitimately laudatory, so please read on.I posted a brief review on Quora.com -- where I first read about the book -- as follows:"Interesting reading. I was expecting/hoping for a little more in the way of case studies, food for thought about conceptualization of data requirements and use of big data, etc. However, if you have an entrepreneurial bent or are interested in understanding more about how some of the number wizards look at industry uses of data, it's worth a read."To amplify on this take, I'd like to make it clear that I approached it from a general reader's perspective... as a potential user of big data and as someone looking to learn more about how to make the leap from owning a bucketful of information to turning it into real knowledge. That kind of work is still needed; this isn't it. The worlds of data science and customers of the fruits of data science still are pretty widely separated.That said, this book appears to be an excellent atlas to the specialty and the solid guide to the best route toward formation of data science practitioners (although definitely outside my experience enough that at least a good chunk of its wisdom probably was lost on me). I also got a sense that it offers insights that might help data scientists become better at reaching out to potential users of their services, which also would be a positive.So, for those in the target audience, possessing at least some of the basic quantitative and analytic skills the field requires, I'd unequivocally endorse this book.
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..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.
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