- Capa comum: 252 páginas
- Editora: Eamon Dolan/Houghton Mifflin Harcourt; Edição: Reprint (4 de março de 2014)
- Idioma: Inglês
- ISBN-10: 0544227751
- ISBN-13: 978-0544227750
- Dimensões do produto: 13,5 x 2 x 20,3 cm
- Peso do produto: 227 g
- Avaliação média: 1 avaliação de cliente
- Lista de mais vendidos da Amazon: no. 82,435 em Livros (Conheça o Top 100 na categoria Livros)
Big Data: A Revolution That Will Transform How We Live, Work, and Think (Inglês) Capa Comum – 3 mar 2014
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Illuminating and very timely . . . a fascinating and sometimes alarming survey of big data s growing effect on just about everything: business, government, science and medicine, privacy, and even on the way we think. New York Times
It seems like big data is in the news every day, as we read the latest examples of how powerful algorithms are teasing out the hidden connections between seemingly unrelated things. Whether it is used by the NSA to fight terrorism or by online retailers to predict customers buying patterns, big data is a revolution occurring around us, in the process of forever changing economics, science, culture, and the very way we think. But it also poses new threats, from the end of privacy as we know it to the prospect of being penalized for things we haven t even done yet, based on big data s ability to predict our future behavior. What we have already seen is just the tip of the iceberg.
Big Data is the first major book about this earthshaking subject, with two leading experts explaining what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards.
An optimistic and practical look at the Big Data revolution just the thing to get your head around the big changes already underway and the bigger changes to come. Cory Doctorow, boingboing.com
[AU PHOTO] VIKTOR MAYER-SCHONBERGER is Professor of Internet Governance and Regulation at the Oxford Internet Institute, Oxford University. A widely recognized authority on big data, he is the author of over a hundred articles and eight books, of which the most recent is Delete: The Virtue of Forgetting in the Digital Age.
[AU PHOTO] KENNETH CUKIER is the Data Editor of the Economist and a prominent commentator on developments in big data. His writings on business and economics have appeared in Foreign Affairs, the New York Times, the Financial Times, and elsewhere.
Sobre o Autor
VIKTOR MAYER-SCHÖNBERGER is Professor of Internet Governance and Regulation at the Oxford Internet Institute, Oxford University. The co-author of Big Data: A Revolution That Will Transform How We, Live, Work, and Think, he has published over a hundred articles and eight other books, including Delete: The Virtue of Forgetting in the Digital Age. He is on the advisory boards of corporations and organizations around the world, including Microsoft and the World Economic Forum. KENNETH CUKIER is the Data Editor of the Economist and co-author of Big Data: A Revolution That Will Transform How We Live, Work, and Think. His writings on business and economics have appeared in Foreign Affairs, the New York Times, the Financial Times, and elsewhere.
KENNETH CUKIER is the Data Editor of the Economist and co-author of Big Data: A Revolution That Will Transform How We Live, Work, and Think. His writings on business and economics have appeared in Foreign Affairs, the New York Times, the Financial Times, and elsewhere.
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But how does one choose a sample? Some argued that purposefully constructing a sample that was representative of the whole would be most the suitable way forward. But in 1934, Jerzy Neyman, a Polish statistician, forcefully showed that such an approach leads to huge errors. The key to avoid them is to aim for randomness in choosing whom to sample. Statisticians have shown that sampling precision improves most dramatically with randomness, not with increased sample size.
Today a third of all of Amazon's sales are said to result from its recommendation and personalization systems. With these systems, Amazon has driven many competitors out of business: not only large bookstores and music stores, but also local booksellers who thought their personal touch would insulate them from the winds of change.
Will a world of predictions dampen our enthusiasm to greet the sunrise, our desire to put our own human imprint on the world? The opposite is actually more likely. Knowing how actions may play out in the future will allow us to take remedial steps to prevent problems or improve outcomes. We will spot students who are starting to slip long before the final exam. We will detect tiny cancers and treat them before the full-blown disease has a chance to emerge. We will see the liklihood on unwanted teenage pregnancy or a life of crime and intervene to change, as much as we can, that predicted outcome. We will prevent deadly fires from consuming overcrowded New York tenements by knowing which building to inspect first."
1. Sampling was important when collecting data was expensive and difficult, but we now we have access by one means or another to all data.
2. Since we have so much data, the quality of individual data points is not important and we can allow inexactness in measurement processes as long as there isn't a systematic bias.
3. Causality and understanding why things happen is no longer as important as correlation and discovering the correct strategies or course of action based upon large bodies of data.
All of these points could have been made in a hundred pages I think, and reading just the first half of the book would give a reader the basic ideas intended by the author.
While there may be areas in which data analysis can point out solutions to problems, I'm not convinced by the author's assertion that Big Data will make experts in various fields obsolete. I would guess that Big Data will be a tool in the hands of experts, but I don't think we'll find data analysts replacing doctors and subject matter experts on a large scale.
If you are looking to understand what the revolution is all about this book explains it very well without going into too detail about the tools that are used to get there.
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