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3.82 AVERAGE
3.82 AVERAGE
Very solid. Stephens-Davidowitz is open about the methodological issues inherent in big data and presents a number of entertaining observations. The main point of the book is that there are minefields of data out there that are untapped, and the methodology for pursuing these studies needs to continue to improve.
funny
informative
reflective
medium-paced
funny
informative
fast-paced
Very informational, fun facts to learn, cool to get a peek into how data science is being used
informative
inspiring
medium-paced
Initially, I was forced to read this book and wasn’t that enthusiastic about it but after a while, I couldn’t put this book down. Definitely a good read if you have free time and know a bit of coding. The conclusion was also witty
Great first third with compelling opening that draws your interest. Middle section tries to get formal in describing the limitations and pitfalls of Big Data but really isn't that rigorous for a technical audience. Instead the brief analysis circles back to more of the same stories.
The conclusion is downright horrible as the author blatantly ran out of ideas and eloquence. He plainly admits that while burning lots of words to pad the length with an obvious shit-eating grin as he rubs it in the reader's face that they're a sucker for sticking around to the last page. Would've rated it one star higher if not for that ending.
The conclusion is downright horrible as the author blatantly ran out of ideas and eloquence. He plainly admits that while burning lots of words to pad the length with an obvious shit-eating grin as he rubs it in the reader's face that they're a sucker for sticking around to the last page. Would've rated it one star higher if not for that ending.
Bit too many generalisations. But the overall idea is solid.
challenging
informative
reflective
slow-paced
The examples in the book are interesting and thought provoking. I am glad I read (listened). the structure, pacing, and length were less enjoyable.
funny
informative
reflective
medium-paced
A bit surprised by the negative reviews for this book, but I guess I can see where the frustration is coming from. I think your opinion will, in part, be shaped by what you're hoping to get out of this book. As a person who works with data, I found this book helpful in two ways:
1) The book highlights data sources and ways that they can be used that I hadn't thought about before. I could imagine this helping me out with feature engineering for machine learning models or analytics that I might work on in the future. The "creative" part of data is knowing where to look, and for the less creative folks of the world
1) The book highlights data sources and ways that they can be used that I hadn't thought about before. I could imagine this helping me out with feature engineering for machine learning models or analytics that I might work on in the future. The "creative" part of data is knowing where to look, and for the less creative folks of the world