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Analytical Skills for AI and Data Science

Building Skills for an AI-Driven Enterprise

Paperback Engels 2020 9781492060949
Verkooppositie 4176
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While several market-leading companies have successfully transformed their business models by following data- and AI-driven paths, the vast majority have yet to reap the benefits. How can your business and analytics units gain a competitive advantage by capturing the full potential of this predictive revolution? This practical guide presents a battle-tested end-to-end method to help you translate business decisions into tractable prescriptive solutions using data and AI as fundamental inputs.

Author Daniel Vaughan shows data scientists, analytics practitioners, and others interested in using AI to transform their businesses not only how to ask the right questions but also how to generate value using modern AI technologies and decision-making principles. You’ll explore several use cases common to many enterprises, complete with examples you can apply when working to solve your own issues.

- Break business decisions into stages that can be tackled using different skills from the analytical toolbox
- Identify and embrace uncertainty in decision making and protect against common human biases
- Customize optimal decisions to different customers using predictive and prescriptive methods and technologies
- Ask business questions that create high value through AI- and data-driven technologies


Aantal pagina's:275
Hoofdrubriek:IT-management / ICT


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Over Daniel Vaughan

Daniel Vaughan has worked with enterprise web applications for over 12 years. He is currently a software architect for a UK financial institution. An experienced Java developer, Daniel first started working with Google Web Toolkit soon after it was released in 2006 and loved the power and simplicity it bought to web application development. When Ext GWT came along he was an early adopter and has used it as part of several large projects. Daniel currently splits his time between the beautiful tranquility of the Cotswolds, England and the fast-moving city state of Singapore. He enjoys travel, scuba diving, and learning new ideas.

Andere boeken door Daniel Vaughan


Why Analytical Skills for AI?
Use Case-Driven Approach
What This Book Isn’t
Who This Book Is For
What’s Needed
Conventions Used in This Book
Using Code Examples
O’Reilly Online Learning
How to Contact Us

1. Analytical Thinking and the AI-Driven Enterprise
What Is AI?
Why Current AI Won’t Deliver on Its Promises
How Did We Get Here?
The Data Revolution
A Tale of Unrealized Expectations
Analytical Skills for the Modern AI-Driven Enterprise
Key Takeways
Further Reading

2. Intro to Analytical Thinking
Descriptive, Predictive, and Prescriptive Questions
When Predictive Analysis Is Powerful: The Case of Cancer Detection
Descriptive Analysis: The Case of Customer Churn
Business Questions and KPIs
KPIs to Measure the Success of a Loyalty Program
An Anatomy of a Decision: A Simple Decomposition
An Example: Why Did You Buy This Book?
A Primer on Causation
Defining Correlation and Causation
Some Difficulties in Estimating Causal Effects
Uncertainty from Simplification
Uncertainty from Heterogeneity
Uncertainty from Social Interactions
Uncertainty from Ignorance
Key Takeaways
Further Reading

3. Learning to Ask Good Business Questions
From Business Objectives to Business Questions
Descriptive, Predictive, and Prescriptive Questions
Always Start with the Business Question and Work Backward
Further Deconstructing the Business Questions
Example with a Two-Sided Platform
Learning to Ask Business Questions: Examples from Common Use Cases
Lowering Churn
Cross-Selling: Next-Best Offer
CAPEX Optimization
Store Locations
Who Should I Hire?
Delinquency Rates
Stock or Inventory Optimization
Store Staffing
Key Takeaways
Further Reading

4. Actions, Levers, and Decisions
Understanding What Is Actionable
Physical Levers
Human Levers
Why Do We Behave the Way We Do?
Levers from Restrictions
Levers That Affect Our Preferences
Levers That Change Your Expectations
Revisiting Our Use Cases
Customer Churn
Capital Expenditure (CAPEX) Optimization
Store Locations
Who Should I Hire?
Delinquency Rates
Stock Optimization
Store Staffing
Key Takeaways
Further Reading

5. From Actions to Consequences: Learning How to Simplify
Why Do We Need to Simplify?
First- and Second-Order Effects
Exercising Our Analytical Muscle: Welcome Fermi
How Many Tennis Balls Fit the Floor of This Rectangular Room?
How Much Would You Charge to Clean Every Window in Mexico City?
Fermi Problems to Make Preliminary Business Cases
Revisiting the Examples from Chapter 3
Customer Churn
CAPEX Optimization
Store Locations
Delinquency Rates
Stock Optimization
Store Staffing
Key Takeaways
Further Reading

6. Uncertainty
Where Does Uncertainty Come From?
Quantifying Uncertainty
Expected Values
Making Decisions Without Uncertainty
Making Simple Decisions Under Uncertainty
Decisions Under Uncertainty
Is This the Best We Can Do?
But This Is a Frequentist Argument
Normative and Descriptive Theories of Decision-Making
Some Paradoxes in Decision-Making Under Uncertainty
The St. Petersburg Paradox
Risk Aversion
Putting it All into Practice
Estimating the Probabilities
Estimating Expected Values
Frequentist and Bayesian Methods
Revisiting Our Use Cases
Customer Churn
CAPEX Optimization
Store Locations
Who to Hire
Delinquency Rates
Stock Optimization
Key Takeaways
Further Reading

7. Optimization
What Is Optimization?
Numerical Optimization Is Hard
Optimization Is Not New in Business Settings
Price and Revenue Optimization
Optimization Without Uncertainty
Customer Churn
CAPEX Investment
Optimal Staffing
Optimal Store Locations
Optimization with Uncertainty
Customer Churn
Optimal Staffing
Tricks for Solving Optimization Problems Under Uncertainty
Key Takeaways
Further Reading

8. Wrapping Up
Analytical Skills
Asking Prescriptive Questions
Understanding Causality
Embracing Uncertainty
Tackling Optimization
The AI-Driven Enterprise of the Future
Back to AI
Some Final Thoughts
A. A Brief Introduction to Machine Learning
What Is Machine Learning?
A Taxonomy of ML Models
Supervised Learning
Unsupervised Learning
Semisupervised Learning
Regression and Classification
Making Predictions
Caveats to the Plug-in Approach
Where Do These Functions Come From?
Making Good Predictions
From Linear Regression to Deep Learning
Linear Regression
Neural Networks
A Primer on A/B Testing
Further Reading


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