Kenett Ron S., Redman Thomas C. The Real Work of Data Science: Turning data into information, better decisions, and stronger organizations

Wiley, 2019. — 120 р. — ISBN: 1119570700.The essential guide for data scientists and for leaders who must get more from their data science teams.It is no secret that “data,” broadly defined, is all the rage. And “data science,” including traditional statistics, Bayesian statistics, business intelligence, predictive analytics, Big Data, Machine Learning (ML), and Artificial Intelligence (AI) are enjoying the spotlight. There are plenty of great successes, building on a rich tradition of statistics in government and industry, driven by increasing business needs, more data powered by social media, the Internet of Things (IoT), and the computer power to analyze it. Iconic new companies include Amazon, Facebook, Google, and Uber.A Higher Calling
The Life‐Cycle View
The Organizational Ecosystem
Once Again, Our Goal
The Difference Between a Good Data Scientist and a Great One
Implications
Learn the Business
The Annual Report
SWOTs and Strategic Analysis
The Balanced Scorecard and Key Performance Indicators
The Data Lens
Build Your Network
Implications
Understand the Real Problem
A Telling Example
Understanding the Real Problem
Implications
Get Out There
Understand Context and Soft Data
Identify Sources of Variability
Selective Attention
Memory Bias
Implications
Sorry, but You Can’t Trust the Data
Most Data Is Untrustworthy
Dealing with Immediate Issues
Getting in Front of Tomorrow’s Data Quality Issues
Implications
Make It Easy for People to Understand Your Insights
First, Get the Basics Right
Presentations Get Passed Around
The Best of the Best
Implications
When the Data Leaves Off and Your Intuition Takes Over
Modes of Generalization
Implications
Take Accountability for Results
Practical Statistical Efficiency
Using Data Science to Perform Impact Analysis
Implications
What It Means to Be “Data‐driven”
Data‐driven Companies and People
Traits of the Data‐driven
Traits of the Antis
Implications
Root Out Bias in Decision‐making
Understand Why It Occurs
Take Control on a Personal Level
Solid Scientific Footings
Implications
Teach, Teach, Teach
The Rope Exercise
The “Roll Your Own” Exercise
The Starter Kit of Questions to Ask Data Scientists
Implications
Evaluating Data Science Outputs More Formally
Assessing Information Quality
A Hands‐On Information Quality Workshop
Educating Senior Leaders
Covering the Waterfront
Companies Need a Data and Data Science Strategy
Organizations Are “Unfit for Data”
Get Started with Data Quality
Implications
Putting Data Science, and Data Scientists, in the Right Spots
The Need for Senior Leadership
Building a Network of Data Scientists
Implications
Moving Up the Analytics Maturity Ladder
Implications
The Industrial Revolutions and Data Science
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
Epilogue
Strong Foundations
A Bridge to the Future
Appendixes
Skills of a Data Scientist
Data Defined
Questions to Help Evaluate the Outputs of Data Science
Ethical Considerations and Today’s Data Scientist