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The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists (Englisch) Taschenbuch – 19. Juni 2015

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4 von 5 Sternen 14 Rezensionen aus den USA.

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Über den Autor und weitere Mitwirkende

About the Authors

The four co-authors are all practicing data scientists.

They've worked in places like billion-dollar technology startup Quora, machine learning startup Ayasdi, and e-commerce website Etsy.

Between the four of them, the authors have done things from applying machine learning to public policy under President Obama’s former Chief Scientist to using data-driven methods to find ways to invest multi-billion dollar investment funds.

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Amazon.com: 4.0 von 5 Sternen 14 Rezensionen
4.0 von 5 Sternen Interviews of Data Scientists 21. Mai 2017
Von Sandro Saitta - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
The Data Science Handbook gathers 25 interviews of Data Scientists. Interviews are well done, most questions depending on the previous answer. This gives a nice feeling of discussion between the interviewer and the Data Scientist. On the content side, it provides interesting insights about the job of Data Scientist. The book is however biased towards pioneers in the field spending 14h a day working, which is definitely not representative of the overall Data Scientist population.

The 25 interviews are covering all major Data Science topics, including data preparation, automation, Big Data, the role of the Data Scientist and moving from academy to industry. Although some of the selected Data Scientists are clearly well known (DJ Patil, Hilary Mason, etc.), others are quite new to the field. It looks like they have been interviewed because they knew the editors or work for a “trendy” company. I would have rather chosen to include other key Data Scientists such as Dean Abbott, John Elder, Eric Siegel and Gregory Piatetsky-Shapiro. The book still remains a great source of inspiration for experienced Data Scientists.
5.0 von 5 Sternen Five Stars 29. September 2016
Von Amazon Customer - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
Great book!
5.0 von 5 Sternen Five Stars 18. März 2016
Von Vibhor Nigam - Veröffentlicht auf Amazon.com
Format: Taschenbuch Verifizierter Kauf
Awesome insights about being a data scientist
13 von 14 Kunden fanden die folgende Rezension hilfreich
3.0 von 5 Sternen Rewarding for future data science specialists; a tough read for those lacking some basic knowledge of the fundamentals 6. Juli 2015
Von T. Farrier - Veröffentlicht auf Amazon.com
Format: Taschenbuch
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. For others (like me), it can serve as a means of understanding at least some of the skill set that can be expected of data science practitioners. However, it's a lot tougher read without at least some background in the area, and I have a strong sense that I didn't get everything out of it that was there for data science cognoscenti.
17 von 20 Kunden fanden die folgende Rezension hilfreich
1.0 von 5 Sternen Useless, toxic 16. Juli 2015
Von Greg M - Veröffentlicht auf Amazon.com
Format: Taschenbuch
I'm a software engineer working in machine learning. I guess you could call that data science. I expected a book of case studies, refined techniques, and insights I could use in my own work. Instead I got this substanceless fluff, a huge collection of softball interview questions ("What do you do? How did you get where you are? Where are you going?"), and an unpleasant sampling of startup culture. When the second interviewee in a row strongly recommended cutting back on sleep in order to get more work done, I skimmed through the rest of it and threw it out in disgust.
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