book

Hands-On Prescriptive Analytics

Name: Hands-On Prescriptive Analytics
Author: Walter R. Paczkowski
ISBN: 9781098153175

by Walter R. Paczkowski

October 2024

Intermediate to advanced

412 pages

11h 1m

English

O'Reilly Media, Inc.

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Includes

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Preface
The Book’s TopicThe Book’s AudienceWhat You Will Learn–and How to Apply ItThe Book’s StructureConventions Used in This BookO’Reilly Online LearningHow to Contact UsAcknowledgments
I. Introduction and Background
1. An Analytical Framework
A Decision-Making FrameworkThe Analytics EvolutionThe Data Science SplitThe Information GlueHow the Analytics Fit TogetherDescriptive AnalyticsPredictive AnalyticsUncertainty: Multiple Sources and ProblemsUncertainty Versus RiskThe Menu Cost of UncertaintyProbabilities and UncertaintyPrescriptive Analytics as a Separate DisciplineDecision-Makers’ OptionsBusiness Goals and ConstraintsKey Decision Question: What Is the Best Decision?The Analytics FlowPrescriptive Analytics and Decision MakingSummary
2. Prescriptive Methods: Overview
Introduction to Prescriptive Analytics MethodsProprietary MethodsPublic Domain MethodsSummary of Prescriptive Analytics MethodsMethods Reviewed in the LiteratureThree Categories of MethodsUmbrella Classes: Non-Stochastic and StochasticDefinition of Non-Stochastic and Stochastic MethodsExamples Based on Scale-ViewsThe Role of Operations ResearchSummary
II. Essential Background Material
3. Python Essentials
Python Structure: OverviewWorldwide CommunityExtensive Array of PackagesEasy to UseOperating SystemsA Programming LanguageIt Is FreePython BasicsNaming ConventionsPython Data StructuresIterablesBasic Python OperatorsIntroduction to Built-In FunctionsIntroduction to User-Defined FunctionsConditional Statements: if-elsePython Looping Constructsfor loopswhile loopsPython PackagesData ManagementData VisualizationStatistical Analysis and ModelingWorking with Python PackagesUsing AnacondaUpdating Python PackagesInstalling PackagesImporting PackagesGo-To ReferencesSummary
4. Probability Essentials
The World Is Ruled by ProbabilitiesWhat Are Probabilities?Fundamental Probability ConceptsFrequency-Based ProbabilitiesCounting FunctionsIndependence and Conditional ProbabilitySummary of Probability RulesLimit Definition of ProbabilitiesSubjective-Based Probabilities: IntroductionBayes’ Theorem: DerivationBayes’ Theorem: Python ImplementationProbability Distributions: OverviewThree Basic Probability Distributions: Binomial, Uniform, and NormalKey Distribution ParametersSummary
III. Non-Stochastic Prescriptive Analytic Methods
5. Mathematical Programming: Overview
BackgroundReason for PopularityThe Objective Function: Prescriptive Analytics GoalLinear ProgrammingTechnical OverviewMenus and Linear ProgrammingPython Use-CasesInteger ProgrammingTechnical OverviewMenus and Integer ProgrammingPython Use-CasesMixed Integer ProgrammingTechnical OverviewPython Use-CaseSummary
6. Decision Tree Analysis: Overview
Extending the Menu into TimeIntroduction to Decision TreesClarification of Decision TreesBackgroundUse of Trees in DecisionsComparing the Two Decision TreesPython Use-CaseUse-Case BackgroundUse-Case Detailed DataRole of the DADTDADT AnalysisReaching a Decision Using a DADTSummaryDADT Function

IV. Stochastic Prescriptive Analytic Methods
7. Simulation Essentials
What Is a Simulation?The Simulation AgeTypes of SimulationsNon-Stochastic Simulations: The ProcessStochastic Simulations: The ProcessPseudo-Random Number Generators: A Brief IntroductionSimulation Models: OverviewKPMs to MeasureAggregation MethodsThe Need for Stochastic SimulationsThe Extent of UncertaintyRandomness and the Degree of UncertaintySummary
8. Simulation Examples
Example 1: Coin TossExample 2: Die TossExample 3: Regression AnalysisExample 4: Mathematical ProgrammingExample 5: Decision TreeSummary
9. Developing Menu Options
The Nature of What-If QuestionsMenu Generating Questions: A Deep DiveThe Structure of What-If QuestionsWhat-If Analysis Versus Sensitivity AnalysisNon-Stochastic Use-CasesPricing What-If Analysis: BasicPricing What-If Analysis: AdvancedStochastic Use-Case: Synthetic DataSpecifying the Process to SimulateExample of a System’s Process FlowSummary
10. Developing Menu Priors
BackgroundDigression on Beliefs and PriorsDeveloping Probability WeightsEliciting Probability Distributions of BeliefsElicitation Method 1: Experimental Design-BasedElicitation Method 2: Direct QuestioningElicitation Method 3: ActivitiesAnalyzing Elicited Probability DistributionsElicitation Analysis 1: AveragingElicitation Analysis 2: CoDAElicitation Analysis 3: BootstrappingPython Use-CaseElicitation Example Analysis 1: AveragingElicitation Example Analysis 2: CoDAElicitation Example Analysis 3: BootstrappingSummary
11. One-Time Decisions
Evidence of the ProblemSequential Decisions: IntroductionSequential Decisions 1: The Business CaseSequential Decisions 2: Post-Business CaseSequential Analysis: Advanced FrameworkMarkov Decision ProblemSimulations and Reinforcement LearningAutomating Sequential Decision MakingSummary
Glossary
Bibliography
Index
About the Author

Content preview from Hands-On Prescriptive Analytics

Part III. Non-Stochastic Prescriptive Analytic Methods

In this book’s third part, I will focus on non-stochastic methods for Prescriptive Analytics. My primary focus will be on Mathematical Programming and Decision Trees. The former is prominent in the Prescriptive Analytics literature, and the latter is a key tool in the Operations Research literature. There are two chapters in this part:

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Hands-On Prescriptive Analytics

by Walter R. Paczkowski

Part III. Non-Stochastic Prescriptive Analytic Methods

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.