Название: The Real Work of Data Science: Turning data into information, better decisions, and stronger organizations
Автор: Ron S. Kenett, Thomas C. Redman
Издательство: Wiley
ISBN: 1119570700
Год: 2019
Страниц: 121
Язык: английский
Формат: pdf (true), djvu
Размер: 24.6 MB

The essential guide for data scientists and for leaders who must get more from their data science teams.

It is no secret that “data,” broadly defined, is all the rage. And “data science,” including traditional statistics, Bayesian statistics, business intelligence, predictive analytics, Big Data, Machine Learning (ML), and Artificial Intelligence (AI) are enjoying the spotlight. There are plenty of great successes, building on a rich tradition of statistics in government and industry, driven by increasing business needs, more data powered by social media, the Internet of Things (IoT), and the computer power to analyze it. Iconic new companies include Amazon, Facebook, Google, and Uber.

It is a great time for data science! The "Economist" boldly claims that data are now "the world's most valuable resource." But, as Kenett and Redman so richly describe, unlocking that value requires far more than technical excellence. The Real Work of Data Science explores understanding the problems, dealing with quality issues, building trust with decision makers, putting data science teams in the right organizational spots, and helping companies become data-driven. This is the work that spells the difference between a good data scientist and a great one, between a team that makes marginal contributions and one that drives the business, between a company that gains some value from its data and one in which data truly is "the most valuable resource."

"I like your book. The chapters address problems that have faced statisticians for generations, updated to reflect today's issues, such as computational Big Data." -Sir David Cox, Warden of Nuffield College and Professor of Statistics, Oxford University

"Data science is critical for competitiveness, for good government, for correct decisions. But what is data science? Kenett and Redman give, by far, the best introduction to the subject I have seen anywhere. They address the critical questions of formulating the right problem, collecting the right data, doing the right analyses, making the right decisions, and measuring the actual impact of the decisions. This book should become required reading in statistics and computer science departments, business schools, analytics institutes and, most importantly, by all business managers." -A. Blanton Godfrey, Joseph D. Moore Distinguished University Professor, Wilson College of Textiles, North Carolina State University