The real work of data science : turning data into information, better decisions, and stronger organizations / Ron S. Kenett, Thomas C. Redman.

Format:

Book

Author/Creator:

Redman, Thomas C., author.

Language:

English

Subjects (All):

Database management--Quality control.

Database management.

Electronic data processing.

Data mining.

Physical Description:

1 online resource (115 pages)

Edition:

1st ed.

Place of Publication:

Hoboken, N.J.: Wiley, 2019.

Hoboken, NJ : John Wiley & Sons, Inc., 2019.

Summary:

"The essential guide for data scientists and for leaders who must get more from their data science teams. 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. Individual data scientists must fully extend themselves. They must make sure they understand the real problems their companies and agencies face, they must build trust with decision-makers, deal with quality issues, help decision makers become more demanding customers of data science, and they must teach their colleagues how to understand and interpret data science--even conduct basic analyses themselves. Further up in the management chain, managers of data science teams must help senior leaders understand where data and data science fit, ensure their teams are placed in the right spots organizationally, and put in place programs that help the entire organization become data-driven. This Kenett and Redman claim, is the 'real work of data science.' And it is this work that will 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'"-- Provided by publisher.

Contents:

A higher calling

The difference between a good data scientist and a great one

Learn the business

Understand the real problem

Get out there

Sorry, but you can't trust the data

Make it easy for people to understand your insights

"When the data leaves off and your intuition takes over

Take accountability for results

What does it mean to be 'data-driven'

Rooting out bias in decision-making

Teach, teach, teach

Evaluating data science outputs more formally

Educating senior leaders

Putting data science, and data scientists, in the right spots

Moving up the analytics maturity ladder

The industrial revolutions and data science

Epilogue

Appendix A. Skills of the data scientist

Appendix B. Data defined

Appendix C. Questions to help evaluate the outputs of data science

Appendix D. Ethical considerations and today's data scientist

Appendix E. Recent technical advances in data science.

Notes:

Includes bibliographical references (p. [101]-106) and index

Description based on print version record.

ISBN:

1-119-57076-X

1-119-57079-4

1-119-57071-9

OCLC:

1090813038