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The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists, by Carl Shan, William Chen, Henry Wang, Max Song
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The Data Science Handbook contains interviews with 25 of the world s best data scientists. We sat down with them, had in-depth conversations about their careers, personal stories, perspectives on data science and life advice. In The Data Science Handbook, you will find war stories from DJ Patil, US Chief Data Officer and one of the founders of the field. You ll learn industry veterans such as Kevin Novak and Riley Newman, who head the data science teams at Uber and Airbnb respectively. You ll also read about rising data scientists such as Clare Corthell, who crafted her own open source data science masters program. This book is perfect for aspiring or current data scientists to learn from the best. It s a reference book packed full of strategies, suggestions and recipes to launch and grow your own data science career.
- Sales Rank: #94293 in Books
- Published on: 2015-06-19
- Original language: English
- Dimensions: 9.00" h x .78" w x 6.00" l,
- Binding: Paperback
- 346 pages
Review
The authors of this book have a knack for getting data scientists to open up and share their wisdom about skill and career development, the role of data scientists in organizations, and more. If you're an aspiring data scientist, The Data Science Handbook will provide a great view of the landscape of this new career path, and how to succeed as a data scientist. If you're leading data science teams, this book can shed light on how to work with and develop data scientists and how data scientists can be set up to succeed. --Harlan Harris (Dir. of Data Science at Education Advisory Board)
This book is written by data scientists for data scientists. The Data Science Handbook offers practical, sound advice, from the top industry experts who've collectively shaped data science into what it is today. As a data scientist, I enjoyed the book's deep interviews that cover data science career paths, application, and building data driven cultures/teams in a very relevant fashion. I think that newcomers and experienced practitioners alike will benefit from reading. --Ferris Jumah (LinkedIn Data Scientist)
The Data Science Handbook collects the wisdom and insights of many experienced data scientists. Their stories are absolutely fascinating, and their career advice is invaluable to anyone who aspires to enter the field. In particular, a PhD student who is trying to decide whether to become a data scientist will find helpful tips on which skills to polish and what classes to take, and will draw inspiration from the life lessons imparted in this collection of interviews. --Yvonne Kung (Stanford Physics PhD Candidate)
About the Author
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.
Most helpful customer reviews
12 of 13 people found the following review helpful.
Rewarding for future data science specialists; a tough read for those lacking some basic knowledge of the fundamentals
By T. Farrier
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.
12 of 15 people found the following review helpful.
Useless, toxic
By Greg M
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.
4 of 4 people found the following review helpful.
Great book for those aspiring to become data scientists!!!!!
By KṛṣṇaKānt
I am new to the field of Analytics/Data Science. This book has given me some invaluable advice as to how to reach my goal of becoming a data scientist. If I can accomplish at least 50% of what these top-notch data scientists have accomplished, in the next 4-5 years, I'd consider that to be a great achievement. They have shown me the direction and now it's my turn to make it happen.
Top takeaways:
1. Steep learning curve
2. Math's the crux
3. Opportunities abound in DS
4. Industry's better than academia
Thank you, guys. This book is worth every cent of the 20 dollars I paid :)
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