Hands-On Prescriptive Analytics: Optimizing Your Decision Making with Python

Business decisions in any context—operational, tactical, or strategic—can have considerable consequences. Whether the outcome is positive and rewarding or negative and damaging to the business, its employees, and stakeholders is unknown when action is approved. These decisions are usually made under the proverbial cloud of uncertainty. With this practical guide, data analysts, data scientists, and business analysts will learn why and how maximizing positive consequences and minimizing negative ones requires three forms of rich information: Descriptive analytics explores the results from an action—what has already happened. Predictive analytics focuses on what could happen. The third, prescriptive analytics, informs us what should happen in the future. While all three are important for decision-makers, the primary focus of this book is on the third: prescriptive analytics. Author Walter R. Paczkowski, Ph.D. shows you: • The distinction among descriptive, predictive, and prescriptive analytics • How predictive analytics produces a menu of action options • How prescriptive analytics narrows the menu of action options • The forms of prescriptive analytics: eight prescriptive methods • Two broad classes of these methods: non-stochastic and stochastic • How to develop prescriptive analyses for action recommendations • Ways to use an appropriate tool-set in Python Cover Copyright Table of Contents Preface The Book’s Topic The Book’s Audience What You Will Learn–and How to Apply It The Book’s Structure Conventions Used in This Book O’Reilly Online Learning How to Contact Us Acknowledgments Part I. Introduction and Background Chapter 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 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 Chapter 2. Prescriptive Methods: Overview Introduction to Prescriptive Analytics Methods Proprietary Methods Public Domain Methods Summary of Prescriptive Analytics Methods Methods Reviewed in the Literature Three Categories of Methods Umbrella Classes: Non-Stochastic and Stochastic Definition of Non-Stochastic and Stochastic Methods Examples Based on Scale-Views The Role of Operations Research Summary Part II. Essential Background Material Chapter 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 Iterables Basic Python Operators Introduction to Built-In Functions Introduction to User-Defined Functions Conditional Statements: if-else Python Looping Constructs for loops while loops Python Packages Data Management Data Visualization Statistical Analysis and Modeling Working with Python Packages Using Anaconda Updating Python Packages Installing Packages Importing Packages Go-To References Summary Chapter 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 Key Distribution Parameters Summary Part III. Non-Stochastic Prescriptive Analytic Methods Chapter 5. Mathematical Programming: Overview Background Reason for Popularity The Objective Function: Prescriptive Analytics Goal Linear Programming Technical Overview Menus and Linear Programming Python Use-Cases Integer Programming Technical Overview Menus and Integer Programming Python Use-Cases Mixed Integer Programming Technical Overview Python Use-Case Summary Chapter 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 Part IV. Stochastic Prescriptive Analytic Methods Chapter 7. Simulation Essentials What Is a Simulation? The Simulation Age Types of Simulations Non-Stochastic Simulations: The Process Stochastic Simulations: The Process Pseudo-Random Number Generators: A Brief Introduction Simulation Models: Overview KPMs to Measure Aggregation Methods The Need for Stochastic Simulations The Extent of Uncertainty Randomness and the Degree of Uncertainty Summary Chapter 8. Simulation Examples Example 1: Coin Toss Example 2: Die Toss Example 3: Regression Analysis Example 4: Mathematical Programming Example 5: Decision Tree Summary Chapter 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 Pricing What-If Analysis: Advanced Stochastic Use-Case: Synthetic Data Specifying the Process to Simulate Example of a System’s Process Flow Summary Chapter 10. Developing Menu Priors Background Digression on Beliefs and Priors Developing Probability Weights Eliciting Probability Distributions of Beliefs Elicitation Method 1: Experimental Design-Based 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 Chapter 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 About the Author Colophon