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Epilogue

“Much fine work in statistics involves minimal mathematics; some bad work in statistics gets
by because of its apparent mathematical content” (Cox 1981, p. 295). This down‐to‐earth
description of work done in statistics, by one of the most important statisticians of this century,
is quite telling. Sir David Cox started his career as a statistician at the Wool Industries Research
Association in Leeds, England. The experiences he gained in this work environment shaped
his extensive groundbreaking work in statistical methodology.
Our key message is that the real work of data science focuses on solving important prob-
lems facing people, companies, and organizations while fully embracing the complexities,
preconceptions, bad data, quirks of decision‐makers, and politics that go with them. This real
work must be rooted in sound theory, or the solutions will not hold up for long. This
combination touches on various domains, some methodological, some technological, some
organizational, and some personal. The 18 chapters in this book cover the nontechnical ones.
To paraphrase Cox’s quote: the real work of data science requires a holistic approach, beyond
computational algorithms and machine learning technologies.
Another famous statistician, John W. Tukey raised the flag half a century ago, calling for a
serious discussion on the future of statistics (Tukey 1962). That future has arrived, and it is
called “data science.”

Strong Foundations

Statistics researchers have pursued the reasoned understanding of data sets for decades.
Among the core discoveries were sampling methods and sufficiency properties, which provide
data scientists the technical foundations for dealing with very large data sets. Research on
topics as diverse as generalized regression and shrinkage estimators continues to put new tools
in the data scientist’s quiver.
Technical advances produced significant game changers. Appendix E provides a brief
overview of recent technical advances. These methods leverage the ever‐growing availability
of big data, featuring many variables and different data structures. These advances have also
raised new ethical concerns, which we discuss in Appendix D.

The Real Work of Data Science: Turning Data into Information, Better Decisions, and Stronger Organizations,
First Edition. Ron S. Kenett and Thomas C. Redman.
© 2019 Ron S. Kenett and Thomas C. Redman. Published 2019 by John Wiley & Sons Ltd.
Companion website: www.wiley.com/go/kenett-redman/datascience