Visualizing Data

Visualizing Data is about visualization
tools that provide deep insight into the
structure of data. There are graphical
tools such as coplots, multiway dot plots,
and the equal count algorithm. There are
fitting tools such as loess and bisquare
that fit equations, nonparametric curves,
and nonparametric surfaces to data.
But the book is much more than just a
compendium of useful tools. It conveys a
strategy for data analysis that stresses
the use of visualization to thoroughly
study the structure of data and to check
the validity of statistical models fitted
to data. The result of the tools and the
strategy is a vast increase in what you can
learn from your data. The book demonstrates
this by reanalyzing many data sets from the
scientific literature, revealing missed
effects and inappropriate models fitted
to data.

William S. Cleveland is the Shanti
S. Gupta Distinguished Professor at
Purdue University, and splits his time
between the Statistics and Computer Science
Departments. Throughout his career, he has
worked in research areas --- statistical
model building, local machine learning,
visualization, time series, and data
mining --- that have broadened the scope
of research in learning from data. He has
developed theories and methods that are
now part of the fundamental knowledge base
of data visualization. He has developed
fundamental tools of machine learning that
were subsequently intensively studied by
researchers both in statistics and computer
science, and widely used by the scientific
community.

Cleveland has published over 100 papers on
his research in a wide range of scientific
journals, books, and proceedings. His two
books, The Elements of Graphing Data and
Visualizing Data have been reviewed in many
journals from a wide variety of disciplines,
and Elements was selected for the Library
of Science.

Cleveland has twice won the Wilcoxon
Prize and once won the Youden prize from
the statistics journal Technometrics. He
is a Fellow of the American Statistical
Association, the Institute of Mathematical
Statistics, and the American Association
of the Advancement of Science, and is
an elected member of the International
Statistical Institute. In 1996 he was
chosen Statistician of the Year by the
Chicago Chapter of the American Statistical
Association.