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Hands-On Prescriptive Analytics
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Hands-On Prescriptive Analytics

by Walter R. Paczkowski

Data Science

Book Details

Book Title

Hands-On Prescriptive Analytics

Author

Walter R. Paczkowski

Publisher

O'Reilly Media

Publication Date

2024

ISBN

9781098153175

Number of Pages

542

Language

English

Format

PDF

File Size

6MB

Subject

data-science

Table of Contents

  • Preface
  • I. Introduction and Background
  • 1. An Analytical Framework
  • A Decision-Making Framework
  • The Analytics Evolution
  • Uncertainty: Multiple Sources and Problems
  • Probabilities and Uncertainty
  • Prescriptive Analytics as a Separate Discipline
  • The Analytics Flow
  • Prescriptive Analytics and Decision Making
  • Summary
  • 2. Prescriptive Methods: Overview
  • Introduction to Prescriptive Analytics Methods
  • Summary of Prescriptive Analytics Methods
  • Umbrella Classes: Non-Stochastic and Stochastic
  • The Role of Operations Research
  • Summary
  • II. Essential Background Material
  • 3. Python Essentials
  • Python Structure: Overview
  • Python Basics
  • Conditional Statements: if-else
  • Python Looping Constructs
  • Python Packages
  • Working with Python Packages
  • Go-To References
  • Summary
  • 4. Probability Essentials
  • The World Is Ruled by Probabilities
  • What Are Probabilities?
  • Fundamental Probability Concepts
  • Limit Definition of Probabilities
  • Subjective-Based Probabilities: Introduction
  • Probability Distributions: Overview
  • Summary
  • III. Non-Stochastic Prescriptive Analytic Methods
  • 5. Mathematical Programming: Overview
  • Background
  • Linear Programming
  • Integer Programming
  • Mixed Integer Programming
  • Summary
  • 6. Decision Tree Analysis: Overview
  • Extending the Menu into Time
  • Introduction to Decision Trees
  • Clarification of Decision Trees
  • Background
  • Python Use-Case
  • Reaching a Decision Using a DADT
  • Summary
  • DADT Function
  • IV. Stochastic Prescriptive Analytic Methods
  • 7. Simulation Essentials
  • What Is a Simulation?
  • Non-Stochastic Simulations: The Process
  • Stochastic Simulations: The Process
  • The Need for Stochastic Simulations
  • Summary
  • 8. Simulation Examples
  • Example 1: Coin Toss
  • Example 2: Die Toss
  • Example 3: Regression Analysis
  • Example 4: Mathematical Programming
  • Example 5: Decision Tree
  • Summary
  • 9. Developing Menu Options
  • The Nature of What-If Questions
  • Menu Generating Questions: A Deep Dive
  • Non-Stochastic Use-Cases
  • Stochastic Use-Case: Synthetic Data
  • Summary
  • 10. Developing Menu Priors
  • Background
  • Digression on Beliefs and Priors
  • Developing Probability Weights
  • Eliciting Probability Distributions of Beliefs
  • Analyzing Elicited Probability Distributions
  • Python Use-Case
  • Summary
  • 11. One-Time Decisions
  • Evidence of the Problem
  • Sequential Decisions: Introduction
  • Sequential Analysis: Advanced Framework
  • Automating Sequential Decision Making
  • Summary
  • Glossary
  • Bibliography
  • Index
  • About the Author