book

Analytical Skills for AI and Data Science

by Daniel Vaughan

May 2020

Beginner to intermediate

242 pages

7h 17m

English

O'Reilly Media, Inc.

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Why Analytical Skills for AI?Use Case-Driven ApproachWhat This Book Isn’tWho This Book Is ForWhat’s NeededConventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
What Is AI?Why Current AI Won’t Deliver on Its PromisesHow Did We Get Here?The Data RevolutionA Tale of Unrealized ExpectationsAnalytical Skills for the Modern AI-Driven EnterpriseKey TakewaysFurther Reading
Descriptive, Predictive, and Prescriptive QuestionsWhen Predictive Analysis Is Powerful: The Case of Cancer DetectionDescriptive Analysis: The Case of Customer ChurnBusiness Questions and KPIsKPIs to Measure the Success of a Loyalty ProgramAn Anatomy of a Decision: A Simple DecompositionAn Example: Why Did You Buy This Book?A Primer on CausationDefining Correlation and CausationSome Difficulties in Estimating Causal EffectsUncertaintyUncertainty from SimplificationUncertainty from HeterogeneityUncertainty from Social InteractionsUncertainty from IgnoranceKey TakeawaysFurther Reading
From Business Objectives to Business QuestionsDescriptive, Predictive, and Prescriptive QuestionsAlways Start with the Business Question and Work BackwardFurther Deconstructing the Business QuestionsExample with a Two-Sided PlatformLearning to Ask Business Questions: Examples from Common Use CasesLowering ChurnCross-Selling: Next-Best OfferCAPEX OptimizationStore LocationsWho Should I Hire?Delinquency RatesStock or Inventory OptimizationStore StaffingKey TakeawaysFurther Reading
Understanding What Is ActionablePhysical LeversHuman LeversWhy Do We Behave the Way We Do?Levers from RestrictionsLevers That Affect Our PreferencesLevers That Change Your ExpectationsRevisiting Our Use CasesCustomer ChurnCross-SellingCapital Expenditure (CAPEX) OptimizationStore LocationsWho Should I Hire?Delinquency RatesStock OptimizationStore StaffingKey TakeawaysFurther Reading
Why Do We Need to Simplify?First- and Second-Order EffectsExercising Our Analytical Muscle: Welcome FermiHow Many Tennis Balls Fit the Floor of This Rectangular Room?How Much Would You Charge to Clean Every Window in Mexico City?Fermi Problems to Make Preliminary Business CasesRevisiting the Examples from Chapter 3Customer ChurnCross-SellingCAPEX OptimizationStore LocationsDelinquency RatesStock OptimizationStore StaffingKey TakeawaysFurther Reading
Where Does Uncertainty Come From?Quantifying UncertaintyExpected ValuesMaking Decisions Without UncertaintyMaking Simple Decisions Under UncertaintyDecisions Under UncertaintyIs This the Best We Can Do?But This Is a Frequentist ArgumentNormative and Descriptive Theories of Decision-MakingSome Paradoxes in Decision-Making Under UncertaintyThe St. Petersburg ParadoxRisk AversionPutting it All into PracticeEstimating the ProbabilitiesEstimating Expected ValuesFrequentist and Bayesian MethodsRevisiting Our Use CasesCustomer ChurnCross-SellingCAPEX OptimizationStore LocationsWho to HireDelinquency RatesStock OptimizationKey TakeawaysFurther Reading
What Is Optimization?Numerical Optimization Is HardOptimization Is Not New in Business SettingsPrice and Revenue OptimizationOptimization Without UncertaintyCustomer ChurnCross-SellingCAPEX InvestmentOptimal StaffingOptimal Store LocationsOptimization with UncertaintyCustomer ChurnCross-SellingOptimal StaffingTricks for Solving Optimization Problems Under UncertaintyKey TakeawaysFurther Reading
Analytical SkillsAsking Prescriptive QuestionsUnderstanding CausalitySimplifyEmbracing UncertaintyTackling OptimizationThe AI-Driven Enterprise of the FutureBack to AISome Final Thoughts
What Is Machine Learning?A Taxonomy of ML ModelsSupervised LearningUnsupervised LearningSemisupervised LearningRegression and ClassificationMaking PredictionsCaveats to the Plug-in ApproachWhere Do These Functions Come From?Making Good PredictionsFrom Linear Regression to Deep LearningLinear RegressionNeural NetworksA Primer on A/B TestingFurther Reading

Overview

While several market-leading companies have successfully transformed their business models by following data- and AI-driven paths, the vast majority have yet to reap the benefits. How can your business and analytics units gain a competitive advantage by capturing the full potential of this predictive revolution? This practical guide presents a battle-tested end-to-end method to help you translate business decisions into tractable prescriptive solutions using data and AI as fundamental inputs.

Author Daniel Vaughan shows data scientists, analytics practitioners, and others interested in using AI to transform their businesses not only how to ask the right questions but also how to generate value using modern AI technologies and decision-making principles. You’ll explore several use cases common to many enterprises, complete with examples you can apply when working to solve your own issues.

Break business decisions into stages that can be tackled using different skills from the analytical toolbox
Identify and embrace uncertainty in decision making and protect against common human biases
Customize optimal decisions to different customers using predictive and prescriptive methods and technologies
Ask business questions that create high value through AI- and data-driven technologies