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659 reviews for:
Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are
Steven Pinker, Seth Stephens-Davidowitz
3.82 AVERAGE
659 reviews for:
Everybody Lies: Big Data, New Data, and What the Internet Can Tell Us About Who We Really Are
Steven Pinker, Seth Stephens-Davidowitz
3.82 AVERAGE
Cartea nu prea te învață multe lucruri practice, în schimb afli multe fun facts despre ce caută lumea pe internet și insecuritățile pe care acestia le au. Cred că este o carte ce nu prea rezonează cu momentul actual datorită complexității si multitudinilor de variabile, însă un lucru cu care te poate ajuta big data este crearea unui mic procent de control asupra mediului
Interesting, but a bit light; with a questionable narrator.
A number of people seem to have a distaste for the narrator; I like him, but the pieces that would make someone else feel otherwise are plain to see. Considering a variety of things he says/jokes that might not land, the controversial topics and his methods of talking about them, and some of the individuals he interacts with. On structure; while all the pieces of the text here are solid enough on their own, they don't flow together very well, and while the author pines towards this greater idea of data science it dosen't seem to find itself quite in this book.
If you find the concept of big data interesting; Stephens does a good enough job of explaining it and the potential it can go in. There's still the usual arrogance/aloof(ness?) you'll find from economists, but it's not as egregious as with most other authors. All the crique aside, this might push me to pursue data science more thouroughly, as Stephens shed's some light on a subject that is more often referenced than it is explained.
Recommend for those interested in big data. Read a sample before committing.
A number of people seem to have a distaste for the narrator; I like him, but the pieces that would make someone else feel otherwise are plain to see. Considering a variety of things he says/jokes that might not land, the controversial topics and his methods of talking about them, and some of the individuals he interacts with. On structure; while all the pieces of the text here are solid enough on their own, they don't flow together very well, and while the author pines towards this greater idea of data science it dosen't seem to find itself quite in this book.
If you find the concept of big data interesting; Stephens does a good enough job of explaining it and the potential it can go in. There's still the usual arrogance/aloof(ness?) you'll find from economists, but it's not as egregious as with most other authors. All the crique aside, this might push me to pursue data science more thouroughly, as Stephens shed's some light on a subject that is more often referenced than it is explained.
Recommend for those interested in big data. Read a sample before committing.
S`okay
Big data is a biig new topic and this book serves as a good introduction. There are some great details and anecdotes.
Big data is a biig new topic and this book serves as a good introduction. There are some great details and anecdotes.
Extremely interesting read about what our data tells about us, regardless of what we say. People can lie, but their data reveals the truth
I waited a week or so to write this review because I wondered if I was a bit too harsh with my 2-stars. However, I’m already finding most of the information to be pretty forgettable. This book had such potential to be great. I was excited to read it, especially since I was a social data analyst in my former life (aka pre-children). Numbers are my friend. I love numbers so, so much. But with this book I found myself losing interest almost immediately. I finished it only because I am committed to completing all book club reads this year, otherwise this would have been a DNF for me.
I found the author to be very full of himself, name-dropping throughout the entire book, and filling the pages with “fluff” that has no real value. A lot of his findings are poorly researched or incomplete, forcing him to use language such as “I believe” or “at least to me”. Real science should not be based on assumptions.
If you’re looking to read a book full of odd (sometimes interesting, sometimes inappropriate) tidbits, this book might be for you. It was definitely not for me.
I found the author to be very full of himself, name-dropping throughout the entire book, and filling the pages with “fluff” that has no real value. A lot of his findings are poorly researched or incomplete, forcing him to use language such as “I believe” or “at least to me”. Real science should not be based on assumptions.
If you’re looking to read a book full of odd (sometimes interesting, sometimes inappropriate) tidbits, this book might be for you. It was definitely not for me.
So. Much. Data.
If you want to keep your cards to your chest and not tell anyone what you’re doing and thinking and wanting, that’s probably the smart move. If you don’t care what happens to your data, give it to Google, so scientists can tell me weird and wonderful and creepy stuff about people. Big Data is as fun as it is concerning. I kind of want to be a data analyst now and find out, how people work.
If Stephens-Davidowitz ever writes that second book he proposes in the conclusion, I’d like to see Mary Roach co-write it. It would be amazing.
If you want to keep your cards to your chest and not tell anyone what you’re doing and thinking and wanting, that’s probably the smart move. If you don’t care what happens to your data, give it to Google, so scientists can tell me weird and wonderful and creepy stuff about people. Big Data is as fun as it is concerning. I kind of want to be a data analyst now and find out, how people work.
If Stephens-Davidowitz ever writes that second book he proposes in the conclusion, I’d like to see Mary Roach co-write it. It would be amazing.
3.5 stars
You may come across as liberal to the world but secretly google racist jokes…..
Although you may spill your deepest darkest secrets to Google, make no mistake this data sits somewhere ready to be analysed.
I work with big data every day, so I was immediately drawn to this book. But you really don’t need to be in the data industry to appreciate the book. It is written for the layman with humour and interesting titbits sprinkled throughout the book.
The first 3rd of the book gives a notable amount of space to data on sex, politics and racism. These are things we are not always honest about with our friends (or even ourselves).
The one downside for me was that the data was very USA centric so some of the case studies dealing with baseball or basketball was just not very interesting to me. There were also one or two graphs that were not properly explained. The author is also very passionate about this subject matter which means he sometimes flinted around from one topic to another in such quick succession that you almost lose the point he is trying to make
But there were sections that I also found fascinating. The explanation of doppelganger search algorithms (this is how Amazon and Netflix suggest books/movies you may like) and its applications across various industries. The case study on how race horses were chosen and how new data also has its limitations were just as great.
This is a little like Freakonomics for Big Data and the author himself is clearly a HUGE fan of Steven Levitt as the conclusion read like an ode to his idol.
And finally, last week the world got its first glimpse of a black hole, only possible due to the crunching of HUGE data using sophisticated algorithms. Thanks Katie Bouman! This shows just how powerful the application of big data can be.
The big questions and mysteries of our time may very well be answered one data set at a time.
Recommended
You may come across as liberal to the world but secretly google racist jokes…..
Although you may spill your deepest darkest secrets to Google, make no mistake this data sits somewhere ready to be analysed.
I work with big data every day, so I was immediately drawn to this book. But you really don’t need to be in the data industry to appreciate the book. It is written for the layman with humour and interesting titbits sprinkled throughout the book.
The first 3rd of the book gives a notable amount of space to data on sex, politics and racism. These are things we are not always honest about with our friends (or even ourselves).
The one downside for me was that the data was very USA centric so some of the case studies dealing with baseball or basketball was just not very interesting to me. There were also one or two graphs that were not properly explained. The author is also very passionate about this subject matter which means he sometimes flinted around from one topic to another in such quick succession that you almost lose the point he is trying to make
But there were sections that I also found fascinating. The explanation of doppelganger search algorithms (this is how Amazon and Netflix suggest books/movies you may like) and its applications across various industries. The case study on how race horses were chosen and how new data also has its limitations were just as great.
This is a little like Freakonomics for Big Data and the author himself is clearly a HUGE fan of Steven Levitt as the conclusion read like an ode to his idol.
And finally, last week the world got its first glimpse of a black hole, only possible due to the crunching of HUGE data using sophisticated algorithms. Thanks Katie Bouman! This shows just how powerful the application of big data can be.
The big questions and mysteries of our time may very well be answered one data set at a time.
Recommended
This is probably one of my favorite books of all-time. As someone that's interested in human psychology and big data, I found this book incredibly insightful.
Fascinating use of big data. Tells us more about how people are really thinking than survey data. Well worth the read.
This is the best example of what drives me up a wall in this industry. This is really awful amounts of data extrapolation for purposes the data was not meant for as it's not collected at a fine enough scale to come to these half baked conclusions. Google search data can be interesting if it's taken with a grain of salt, you can't know a person's motivations for googling something, I've googled plenty of things trying to get an answer for someone else. People also lie on the internet. I think this either needed to be more playful or more based in actual scientific methods because this as is is extremely misleading and not really talking about anything in the way it should have been. I wish it had just focused on the election data and not gotten distracted by trying to find all kinds of zany examples of other unexpected search stats as I think he had an interesting thesis there.