Hands-On Prescriptive Analytics

Spis treści książki

• Preface
  • The Books Topic
  • The Books Audience
  • What You Will Learnand How to Apply It
  • The Books Structure
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgments
• I. Introduction and Background
• 1. An Analytical Framework
  • A Decision-Making Framework
  • The Analytics Evolution
    • The Data Science Split
    • The Information Glue
    • How the Analytics Fit Together
    • Descriptive Analytics
    • Predictive Analytics
      • Forms of Predictive Analytics
      • Menu of prediction options
      • Creation of the menus
      • Natural Projects
      • What-if projects
      • Examples of decision menus
        • Vignette 1: Operational scale-view
        • Vignette 2: Tactical scale-view
        • Vignette 3: Strategic scale-view
  • Uncertainty: Multiple Sources and Problems
    • Uncertainty Versus Risk
    • The Menu Cost of Uncertainty
  • Probabilities and Uncertainty
  • Prescriptive Analytics as a Separate Discipline
    • Decision-Makers Options
    • Business Goals and Constraints
    • Key Decision Question: What Is the Best Decision?
  • The Analytics Flow
  • Prescriptive Analytics and Decision Making
  • Summary
• 2. Prescriptive Methods: Overview
  • Introduction to Prescriptive Analytics Methods
    • Proprietary Methods
    • Public Domain Methods
  • Summary of Prescriptive Analytics Methods
    • Methods Reviewed in the Literature
      • Probabilistic models
      • Machine learning and data mining
      • Mathematical programming
      • Evolutionary computation
      • Simulations
      • Logic-based models
    • Three Categories of Methods
      • Mathematical programming
      • Simulation
      • Decision trees
  • Umbrella Classes: Non-Stochastic and Stochastic
    • Definition of Non-Stochastic and Stochastic Methods
    • Examples Based on Scale-Views
  • The Role of Operations Research
  • Summary
• II. Essential Background Material
• 3. Python Essentials
  • Python Structure: Overview
    • Worldwide Community
    • Extensive Array of Packages
    • Easy to Use
    • Operating Systems
    • A Programming Language
    • It Is Free
  • Python Basics
    • Naming Conventions
    • Python Data Structures
      • Tuples
      • Lists
      • Dictionaries
    • Iterables
    • Basic Python Operators
    • Introduction to Built-In Functions
      • enumerate
      • round
      • print
      • range
    • Introduction to User-Defined Functions
      • Positional arguments
      • Named arguments
  • Conditional Statements: if-else
  • Python Looping Constructs
    • for loops
    • while loops
  • Python Packages
    • Data Management
      • NumPy
      • pandas
    • Data Visualization
    • Statistical Analysis and Modeling
      • statsmodels
      • scikit-learn
      • SciPy
  • Working with Python Packages
    • Using Anaconda
    • Updating Python Packages
    • Installing Packages
    • Importing Packages
  • Go-To References
  • Summary
• 4. Probability Essentials
  • The World Is Ruled by Probabilities
  • What Are Probabilities?
  • Fundamental Probability Concepts
    • Frequency-Based Probabilities
    • Counting Functions
    • Independence and Conditional Probability
    • Summary of Probability Rules
  • Limit Definition of Probabilities
  • Subjective-Based Probabilities: Introduction
    • Bayes Theorem: Derivation
    • Bayes Theorem: Python Implementation
  • Probability Distributions: Overview
    • Three Basic Probability Distributions: Binomial, Uniform, and Normal
      • Binomial distribution
      • Uniform distribution
      • Normal distribution
    • Key Distribution Parameters
  • Summary
• III. Non-Stochastic Prescriptive Analytic Methods
• 5. Mathematical Programming: Overview
  • Background
    • Reason for Popularity
    • The Objective Function: Prescriptive Analytics Goal
  • Linear Programming
    • Technical Overview
      • The simplex approach
      • Digression: The production function
      • Other solution methods
      • Handling special issues
    • Menus and Linear Programming
    • Python Use-Cases
      • Operational scale-view: Example 1
      • Operational scale-view: Example 2
  • Integer Programming
    • Technical Overview
    • Menus and Integer Programming
    • Python Use-Cases
      • Tactical scale-view
        • Case 1: Select as many as needed
        • Case 2: Select a specific number
        • Case 2a: Select only one
        • Case 3: Select based on logical conditions
      • Strategic scale-view
        • Case 1: Select as many as needed
        • Case 2: Select a specific number
        • Case 2a: Select only one
        • Case 3: Select based on logical conditions
  • Mixed Integer Programming
    • Technical Overview
    • Python Use-Case
  • Summary
• 6. Decision Tree Analysis: Overview
  • Extending the Menu into Time
  • Introduction to Decision Trees
  • Clarification of Decision Trees
  • Background
    • Use of Trees in Decisions
    • Comparing the Two Decision Trees
  • Python Use-Case
    • Use-Case Background
    • Use-Case Detailed Data
    • Role of the DADT
    • DADT Analysis
  • Reaching a Decision Using a DADT
  • Summary
  • DADT Function
• IV. Stochastic Prescriptive Analytic Methods
• 7. Simulation Essentials
  • What Is a Simulation?
    • The Simulation Age
    • Types of Simulations
      • Non-stochastic
      • Stochastic
  • Non-Stochastic Simulations: The Process
  • Stochastic Simulations: The Process
    • Pseudo-Random Number Generators: A Brief Introduction
      • Linear congruential generator
      • Mersenne Twister generator
      • Generating random numbers in Python
        • random
        • NumPy
    • Simulation Models: Overview
    • KPMs to Measure
    • Aggregation Methods
  • The Need for Stochastic Simulations
    • The Extent of Uncertainty
    • Randomness and the Degree of Uncertainty
  • 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
    • The Structure of What-If Questions
    • What-If Analysis Versus Sensitivity Analysis
  • Non-Stochastic Use-Cases
    • Pricing What-If Analysis: Basic
      • The pricing problem
      • Predictive model estimation
      • What-if analyses
    • Pricing What-If Analysis: Advanced
      • The pricing problem
      • Predictive model estimation
      • What-if analyses
  • Stochastic Use-Case: Synthetic Data
    • Specifying the Process to Simulate
    • Example of a Systems Process Flow
      • The what-if questions
      • A diffusion model
      • Generating synthetic data
      • What-if analysis with the synthetic data
  • Summary
• 10. Developing Menu Priors
  • Background
  • Digression on Beliefs and Priors
  • Developing Probability Weights
  • Eliciting Probability Distributions of Beliefs
    • Elicitation Method 1: Experimental Design-Based
      • A conjoint analysis
      • Discrete choice analysis
      • MaxDiff analysis
      • Methods problem
    • Elicitation Method 2: Direct Questioning
    • Elicitation Method 3: Activities
  • Analyzing Elicited Probability Distributions
    • Elicitation Analysis 1: Averaging
    • Elicitation Analysis 2: CoDA
    • Elicitation Analysis 3: Bootstrapping
  • Python Use-Case
    • Elicitation Example Analysis 1: Averaging
    • Elicitation Example Analysis 2: CoDA
    • Elicitation Example Analysis 3: Bootstrapping
  • Summary
• 11. One-Time Decisions
  • Evidence of the Problem
  • Sequential Decisions: Introduction
    • Sequential Decisions 1: The Business Case
    • Sequential Decisions 2: Post-Business Case
  • Sequential Analysis: Advanced Framework
    • Markov Decision Problem
    • Simulations and Reinforcement Learning
  • Automating Sequential Decision Making
  • Summary
• Glossary
• Bibliography
• Index

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