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The Data Science Handbook
- GenresNonfiction
285 pages, ebook
First published May 1, 2015
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245 people want to read
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Displaying 1 - 10 of 10 reviews
December 13, 2021
I have no doubt that this is probably the fastest book ever written. I'm so annoyed I didn't think of it myself first.
Here's how to do it. Email 25 data scientists from top companies and ask them to fill out a form with some questions: "What's your background?", "How did you end up here at XYZ?", "What constitutes a top data scientist?". Next, copy and paste the replies into a document, format it a bit, and voilá, there's your book.
The first thing you'll learn here is that if you ask mathematicians what constitutes a top data scientist, they'll tell you it is mathematical skills. If you ask engineers, they'll tell you it's engineering skills. Statisticians? You guessed it, it's statistical reasoning. And so on. They are all right, but that doesn't mean the information is highly valuable. Data science is known to encompass a large circle of disciplines that favor or punish skill specificity depending on the industry.
As such, this book could have easily been half the size without losing much, mostly because a lot of space is devoted to personal stories, background, etc. You get a total of 25 completely formulaic interviews that get boring by the time you get to number 10.
The worst thing for me is that if this is the first book you pick up about data science, you might get the wrong idea of the profession: you need to get a Ph.D., you need to work like a madman (or madwoman), you need to learn all the tech and programming languages under the sun, then you might have what it takes... Sure, those things help but they are not the only road.
It could have been a much better read had the author spent time aggregating the information provided and synthesizing it for the curious reader rather than spitting out every section verbatim.
Sometimes interesting. But mostly Meh.
Here's how to do it. Email 25 data scientists from top companies and ask them to fill out a form with some questions: "What's your background?", "How did you end up here at XYZ?", "What constitutes a top data scientist?". Next, copy and paste the replies into a document, format it a bit, and voilá, there's your book.
The first thing you'll learn here is that if you ask mathematicians what constitutes a top data scientist, they'll tell you it is mathematical skills. If you ask engineers, they'll tell you it's engineering skills. Statisticians? You guessed it, it's statistical reasoning. And so on. They are all right, but that doesn't mean the information is highly valuable. Data science is known to encompass a large circle of disciplines that favor or punish skill specificity depending on the industry.
As such, this book could have easily been half the size without losing much, mostly because a lot of space is devoted to personal stories, background, etc. You get a total of 25 completely formulaic interviews that get boring by the time you get to number 10.
The worst thing for me is that if this is the first book you pick up about data science, you might get the wrong idea of the profession: you need to get a Ph.D., you need to work like a madman (or madwoman), you need to learn all the tech and programming languages under the sun, then you might have what it takes... Sure, those things help but they are not the only road.
It could have been a much better read had the author spent time aggregating the information provided and synthesizing it for the curious reader rather than spitting out every section verbatim.
Sometimes interesting. But mostly Meh.
February 27, 2020
This book is a compilation of interviews with prominent Data Scientists. Overall, I thought it was a really interesting read to get so many different perspectives on the field of Data Science and to read through the many pathways by which people have ended up in Data Science (though mostly through physics and statistics haha).
It was 25 interviews, but I think it probably could have been better served with about 20. There were a few that mostly offered perspectives voiced by other interviewees, but just coming from a different background. While interesting to learn about other Data Scientists in general, there were some deeper dives into their specific work that challenged my ability to read with interest.
There were also a few obvious typos, which gave it a bit of a lack of polish. I think this would be a really useful book more for people moving into hiring Data Scientists than those trying to become one. As these people were high in their field, most are in leadership positions and as such, their insights often apply to what they look for in hiring a Data Scientist. It is still helpful to have those views if you are looking to apply to jobs, but I'd say to a lesser degree.
It was 25 interviews, but I think it probably could have been better served with about 20. There were a few that mostly offered perspectives voiced by other interviewees, but just coming from a different background. While interesting to learn about other Data Scientists in general, there were some deeper dives into their specific work that challenged my ability to read with interest.
There were also a few obvious typos, which gave it a bit of a lack of polish. I think this would be a really useful book more for people moving into hiring Data Scientists than those trying to become one. As these people were high in their field, most are in leadership positions and as such, their insights often apply to what they look for in hiring a Data Scientist. It is still helpful to have those views if you are looking to apply to jobs, but I'd say to a lesser degree.
July 17, 2018
I had been holding out hope that data science (or mining plus statistical programming, as it used to be called) could be an intellectual, rarefied place within the private sector, where the practical and the abstract are wed sweetly. It might be, but this book gives you little sense of that. Even the demonstrably brilliant (DJ Patil) talk like third-rate vice-presidents-of-munging.
(You might shrug because you expected no better of computer people, but you are ill-informed: some of the great stylists of the age are programmers first of all.)
In one sentence: Data is Innovation for incentivising proactive momentum-based cultural synthesis change
(You might shrug because you expected no better of computer people, but you are ill-informed: some of the great stylists of the age are programmers first of all.)
In one sentence: Data is Innovation for incentivising proactive momentum-based cultural synthesis change
May 13, 2021
Most of the interviews here are pretty good and informative if you're interested in pursuing data science. It does a good job of illistrating the different tracks and is helpful for discerning if you're more of an engineer or of social science motivated data scientist. However, be prepared for a heavy dose of workism throughout.
August 10, 2019
Picked this up here and there to read a section. Realized after maybe 6 of them that they weren't telling me too much that was helpful for me. Maybe this was more useful 3 or 4 years ago, or maybe I'm not quite the target audience, but it didn't do it for me.
July 22, 2021
No new insights on this book. Maybe if you are just getting started it might be useful.
Data science is such a broadly used term right now, that it is likely this won't help you unless you still have no idea on how DS can be used as a term in conversation.
Data science is such a broadly used term right now, that it is likely this won't help you unless you still have no idea on how DS can be used as a term in conversation.
August 30, 2020
Interesting interviews with professionals in the field. Though feels like interviewees sometimes give vague answers. I couldn't find my answers there.
September 2, 2015
I really liked 3-4 interviews out of the 25. But it was nice overall.
July 13, 2016
Nice book, there are a lot of good interviews. I particularly enjoyed the one from Sean Gourley.
April 29, 2017
great! as a data-scientist-to-be I really enjoyed reading this book and at the mean time I learned a lot of stuff.
there are 25 interviews in the book and they cover all the aspects of the data science field. you can read about the basic skills and backgrounds you need to become a data scientist, the challenges you may face as a DS, the future of the field, and so many amazing stuff such as experiences of the pioneer data scientists of the world.
to sum up: a great book and a must-read book for anyone considering to get a job in the DS world.
there are 25 interviews in the book and they cover all the aspects of the data science field. you can read about the basic skills and backgrounds you need to become a data scientist, the challenges you may face as a DS, the future of the field, and so many amazing stuff such as experiences of the pioneer data scientists of the world.
to sum up: a great book and a must-read book for anyone considering to get a job in the DS world.
Displaying 1 - 10 of 10 reviews