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The Real Work of Data Science
The Real Work of Data Science
Praise for The Real Work of Data Science
About the Authors
Preface
Fad, Trend, or Fundamental Transformation?
Data Scientists and Chief Analytics Officers
Introduction to the Book
About the Companion Website
The Life‐Cycle View
The Organizational Ecosystem
Once Again, Our Goal
Implications
The Annual Report
SWOTs and Strategic Analysis
The Balanced Scorecard and Key Performance Indicators
The Data Lens
Build Your Network
Implications
A Telling Example
Understanding the Real Problem
Implications
Understand Context and Soft Data
Identify Sources of Variability
Selective Attention
Memory Bias
Implications
Most Data Is Untrustworthy
Dealing with Immediate Issues
Getting in Front of Tomorrow's Data Quality Issues
Implications
First, Get the Basics Right
Presentations Get Passed Around
The Best of the Best
Implications
Modes of Generalization
Implications
Practical Statistical Efficiency
Using Data Science to Perform Impact Analysis
Implications
Data‐driven Companies and People
Traits of the Data‐driven
Traits of the Antis
Implications
Understand Why It Occurs
Take Control on a Personal Level
Solid Scientific Footings
Implications
The Rope Exercise
The “Roll Your Own” Exercise
The Starter Kit of Questions to Ask Data Scientists
Implications
Assessing Information Quality
A Hands‐On Information Quality Workshop
Implications
Covering the Waterfront
Companies Need a Data and Data Science Strategy
Organizations Are “Unfit for Data”
Get Started with Data Quality
Implications
The Need for Senior Leadership
Building a Network of Data Scientists
Implications
Implications
The First Industrial Revolution: From Craft to Repetitive Activity
The Second Industrial Revolution: The Advent of the Factory
The Third Industrial Revolution: Enter the Computer
The Fourth Industrial Revolution: The Industry 4.0 Transformation
Implications
Strong Foundations
A Bridge to the Future
Videos, Blogs, and Presentations
Articles
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