- Taschenbuch: 346 Seiten
- Verlag: Data Science Bookshelf, The (19. Juni 2015)
- Sprache: Englisch
- ISBN-10: 0692434879
- ISBN-13: 978-0692434871
- Größe und/oder Gewicht: 15,2 x 2 x 22,9 cm
- Durchschnittliche Kundenbewertung: Schreiben Sie die erste Bewertung
- Amazon Bestseller-Rang: Nr. 140.205 in Fremdsprachige Bücher (Siehe Top 100 in Fremdsprachige Bücher)
The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists (Englisch) Taschenbuch – 19. Juni 2015
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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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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.
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.