Analytical Skills for AI and Data Science

by Daniel Vaughan

Data Science

Book Details

Book Title

Analytical Skills for AI and Data Science

Author

Daniel Vaughan

Publisher

O'Reilly Media, Inc, USA

Publication Date

2020

ISBN

9781492060949

Number of Pages

323

Language

English

Format

PDF

File Size

4.2MB

Subject

data-science

Table of Contents

  • Preface
  • 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?
  • 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
  • Business Questions and KPIs
  • An Anatomy of a Decision: A Simple Decomposition
  • A Primer on Causation
  • Uncertainty
  • 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
  • Learning to Ask Business Questions: Examples from Common Use Cases
  • Key Takeaways
  • Further Reading
  • 4. Actions, Levers, and Decisions
  • Understanding What Is Actionable
  • Physical Levers
  • Human Levers
  • Revisiting Our Use Cases
  • Key Takeaways
  • Further Reading
  • 5. From Actions to Consequences: Learning How to Simplify
  • Why Do We Need to Simplify?
  • Exercising Our Analytical Muscle: Welcome Fermi
  • Revisiting the Examples from Chapter 3
  • Key Takeaways
  • Further Reading
  • 6. Uncertainty
  • Where Does Uncertainty Come From?
  • Quantifying Uncertainty
  • Making Decisions Without Uncertainty
  • Making Simple Decisions Under Uncertainty
  • Decisions Under Uncertainty
  • Normative and Descriptive Theories of Decision-Making
  • Some Paradoxes in Decision-Making Under Uncertainty
  • Putting it All into Practice
  • Revisiting Our Use Cases
  • Key Takeaways
  • Further Reading
  • 7. Optimization
  • What Is Optimization?
  • Optimization Without Uncertainty
  • Optimization with Uncertainty
  • Key Takeaways
  • Further Reading
  • 8. Wrapping Up
  • Analytical Skills
  • The AI-Driven Enterprise of the Future
  • Some Final Thoughts
  • A. A Brief Introduction to Machine Learning
  • What Is Machine Learning?
  • A Taxonomy of ML Models
  • Regression and Classification
  • Making Predictions
  • From Linear Regression to Deep Learning
  • A Primer on A/B Testing
  • Further Reading
  • Index