006: Numbersense by Kaiser Fung

by Gerard

DDC_006

006.312: Fung, Kaiser. Numbersense: How to Use Big Data to Your Advantage. New York: McGraw-Hill, 2013. 210 pp. ISBN 978-0-07-179966-9.

Dewey Breakdown:

  • 000s: Computer science, Information, and General Works
  • 000: Computer science, knowledge, and systems
  • 006: Special computer methods
  • 006.3: Artificial intelligence
  • 006.31: Machine learning
  • 006.312: Data mining

Have you ever read a news report of a new study or statistic and felt instantly skeptical of the findings? These days, information is everywhere, but if you don’t know how to interpret it or at least read it properly, it can become twisted to support many different theories. Kaiser Fung’s Numbersense is an attempt to teach readers just how to mine large sets of data for relevant, true, and reality-based conclusions. While it may not be completely relevant to your life, it does offer a new way of looking at the world.

Fung takes examples from current events and stories to show how data is presented to the public and just how it is derived and manipulated. He looks at the following models:

  • Law schools, and statistical manipulations used to increase their national rankings
  • BMI calculations, and how differing measures can lead to different health findings
  • The Groupon phenomenon, and how it actually hurts local businesses
  • Internet marketing initiatives, and how false positives lead to more spam
  • Unemployment rates, and how seasonality can skew the public’s perception of the economy
  • The Consumer Price Index, and how averaging disparate entities can cause miscalculations
  • Fantasy Football Leagues, and how balance beats flash on the fantasy field

In each of these examples, Fung delves deep into the data to find interesting areas where the common perception can be skewed by how the data is analyzed. The weird thing I kept thinking was if Fung thinks that most data presented is skewed or flawed in some way, how are we to trust him? Isn’t he also presenting seemingly authoritative data? Throughout the book, he touts the quality of “numbersense” (constantly presented in small caps in the text). It’s almost as if he’s trying to sell a new weight loss system or tax program. In the end, though, his examples do lead to new ways of looking at data. This is indeed the era of Big Data; learning how to understand it not a bad skill to have. This book will definitely be of interest to analysts and skeptics, but anyone looking to peek behind the statistical curtain will get something out of it. A curious and quick read.

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