Dark Data Dark Data

Dark Data

Why What You Don’t Know Matters

    • 4.0 • 1 calificación
    • $13.99

Descripción editorial

A practical guide to making good decisions in a world of missing data

In the era of big data, it is easy to imagine that we have all the information we need to make good decisions. But in fact the data we have are never complete, and may be only the tip of the iceberg. Just as much of the universe is composed of dark matter, invisible to us but nonetheless present, the universe of information is full of dark data that we overlook at our peril. In Dark Data, data expert David Hand takes us on a fascinating and enlightening journey into the world of the data we don't see.

Dark Data explores the many ways in which we can be blind to missing data and how that can lead us to conclusions and actions that are mistaken, dangerous, or even disastrous. Examining a wealth of real-life examples, from the Challenger shuttle explosion to complex financial frauds, Hand gives us a practical taxonomy of the types of dark data that exist and the situations in which they can arise, so that we can learn to recognize and control for them. In doing so, he teaches us not only to be alert to the problems presented by the things we don’t know, but also shows how dark data can be used to our advantage, leading to greater understanding and better decisions.

Today, we all make decisions using data. Dark Data shows us all how to reduce the risk of making bad ones.

GÉNERO
Informática e Internet
PUBLICADO
2020
18 de febrero
IDIOMA
EN
Inglés
EXTENSIÓN
344
Páginas
EDITORIAL
Princeton University Press
VENDEDOR
Princeton University Press
TAMAÑO
1.9
MB
On Being a Data Skeptic On Being a Data Skeptic
2013
Doing Data Science Doing Data Science
2013
Creating a Data-Driven Organization Creating a Data-Driven Organization
2015
Data Science for Business Data Science for Business
2013
Thinking with Data Thinking with Data
2014
Big Data Now: 2012 Edition Big Data Now: 2012 Edition
2012
Statistics Statistics
2008
Measurement Measurement
2016
The Improbability Principle The Improbability Principle
2014
Artificial Intelligence Frontiers in Statistics Artificial Intelligence Frontiers in Statistics
2020
From GDP to Sustainable Wellbeing From GDP to Sustainable Wellbeing
2020
Il tradimento dei numeri Il tradimento dei numeri
2019