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The Data Science Handbook: Advice and Insights from 25 Amazing Data Scientists Paperback – June 19, 2015
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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.
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Top Customer Reviews
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.
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 :)
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.
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