book

AI-Assisted Statistics for Data Scientists, 3rd Edition

by Peter Bruce, Andrew Bruce, Peter Gedeck

August 2026

Beginner to intermediate

519 pages

12h 37m

English

O'Reilly Media, Inc.

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Preface
Conventions Used in This BookUsing Code ExamplesO’Reilly Online LearningHow to Contact UsAcknowledgments
1. Exploratory Data Analysis
Elements of Structured DataFurther ReadingData StructuresRectangular DataData Frames and IndexesNonrectangular Data StructuresFurther ReadingData Dictionaries and CatalogsFurther ReadingEstimates of LocationMeanMedian and Robust EstimatesExample: Location Estimates of Population and Murder RatesFurther ReadingEstimates of VariabilityStandard Deviation and Related EstimatesEstimates Based on PercentilesExample: Variability Estimates of State PopulationFurther ReadingExploring the Data DistributionPercentiles and BoxplotsFrequency Tables and HistogramsDensity Plots and EstimatesFurther ReadingExploring Binary and Categorical DataModeExpected ValueProbabilityFurther ReadingCorrelationScatterplotsFurther ReadingExploring Two or More VariablesHexagonal Binning and Contours (Plotting Numeric Versus Numeric Data)Two Categorical VariablesCategorical and Numeric DataVisualizing Multiple VariablesFurther ReadingInterpreting Visualization Results with AISummaryExercises
2. Data and Sampling Distributions
Random Sampling and Sample BiasBiasRandom SelectionSize Versus Quality: When Does Size Matter?Sample Mean Versus Population MeanFurther ReadingSelection BiasRegression to the MeanFurther ReadingSampling Distribution of a StatisticCentral Limit TheoremStandard ErrorFurther ReadingThe BootstrapResampling Versus BootstrappingFurther ReadingConfidence IntervalsFurther ReadingNormal DistributionStandard Normal and QQ-PlotsLong-Tailed DistributionsFurther ReadingStudent’s t-DistributionFurther ReadingBinomial DistributionFurther ReadingChi-Square DistributionFurther ReadingF-DistributionFurther ReadingPoisson and Related DistributionsPoisson DistributionsExponential DistributionEstimating the Failure RateWeibull DistributionFurther ReadingSummaryExploration with AI
3. Statistical Experiments and Significance Testing
A/B TestingWhy Have a Control Group?Why Just A/B? Why Not C, D,…?Further ReadingHypothesis TestsThe Null HypothesisAlternative HypothesisOne-Way Versus Two-Way Hypothesis TestsFurther ReadingResamplingPermutation TestExample: Web StickinessExhaustive and Bootstrap Permutation TestsPermutation Tests: The Bottom Line for Data ScienceFurther ReadingStatistical Significance and p-Valuesp-ValueAlphaType 1 and Type 2 ErrorsData Science and p-ValuesFurther Readingt-TestsFurther ReadingMultiple TestingFurther ReadingDegrees of FreedomFurther ReadingANOVAF-StatisticTwo-Way ANOVAFurther ReadingChi-Square TestChi-Square Test: A Resampling ApproachChi-Square Test: Statistical TheoryFisher’s Exact TestRelevance for Data ScienceFurther ReadingMulti-Arm Bandit AlgorithmFurther ReadingPower and Sample SizeSample SizeFurther ReadingSummaryExploration with AI
4. Regression and Prediction
Simple Linear RegressionThe Regression EquationFitted Values and ResidualsLeast SquaresPrediction Versus Explanation (Profiling)Further ReadingMultiple Linear RegressionExample: King County Housing DataAssessing the ModelCross-ValidationModel Selection and Stepwise RegressionWeighted RegressionFurther ReadingPrediction Using RegressionThe Dangers of ExtrapolationConfidence and Prediction IntervalsFactor Variables in RegressionDummy Variables RepresentationFactor Variables with Many LevelsOrdered Factor VariablesInterpreting the Regression EquationCorrelated PredictorsMulticollinearityConfounding VariablesInteractions and Main EffectsRegression DiagnosticsOutliersInfluential ValuesHeteroskedasticity, Non-Normality, and Correlated ErrorsPartial Residual Plots and NonlinearityPolynomial and Spline RegressionPolynomialSplinesGeneralized Additive ModelsFurther ReadingAnalyzing the Results of a Regression Analysis with AISummaryExploration with AI
5. Classification
Naive BayesWhy Exact Bayesian Classification Is ImpracticalThe Naive SolutionNumeric Predictor VariablesFurther ReadingDiscriminant AnalysisCovariance MatrixFisher’s Linear DiscriminantA Simple ExampleFurther ReadingLogistic RegressionLogistic Response Function and LogitLogistic Regression and the GLMGeneralized Linear ModelsPredicted Values from Logistic RegressionInterpreting the Coefficients and Odds RatiosLinear and Logistic Regression: Similarities and DifferencesAssessing the ModelFurther ReadingEvaluating Classification ModelsConfusion MatrixThe Rare Class ProblemPrecision, Recall, and SpecificityROC CurveAUCLiftFurther ReadingStrategies for Imbalanced DataUndersamplingOversampling and Up/Down WeightingData GenerationCost-Based ClassificationExploring the PredictionsFurther ReadingSummaryExploration with AI
6. Statistical Machine Learning
K-Nearest NeighborsA Small Example: Predicting Loan DefaultDistance MetricsOne Hot EncoderStandardization (Normalization, z-Scores)Choosing KKNN as a Feature EngineTree ModelsA Simple ExampleThe Recursive Partitioning AlgorithmMeasuring Homogeneity or ImpurityStopping the Tree from GrowingPredicting a Continuous ValueHow Trees Are UsedFurther ReadingBagging and the Random ForestBaggingRandom ForestVariable ImportanceHyperparametersBoostingThe Boosting AlgorithmXGBoostRegularization: Avoiding OverfittingHyperparameters and Cross-ValidationSummaryExploration with AI 5
7. Unsupervised Learning
Principal Components AnalysisA Simple ExampleComputing the Principal ComponentsInterpreting Principal ComponentsCorrespondence AnalysisAnalyzing the Results of Principal Component and Correspondence Analysis with AIFurther ReadingK-Means ClusteringA Simple ExampleK-Means AlgorithmInterpreting the ClustersAnalyzing the Clusters with AISelecting the Number of ClustersHierarchical ClusteringA Simple ExampleThe DendrogramThe Agglomerative AlgorithmMeasures of DissimilarityModel-Based ClusteringMultivariate Normal DistributionMixtures of NormalsSelecting the Number of ClustersFurther ReadingScaling and Categorical VariablesScaling the VariablesDominant VariablesCategorical Data and Gower’s DistanceProblems with Clustering Mixed DataReturning to AISummaryExercises
8. Neural Networks
The Basic Structure of a Neural NetFurther ReadingFitting a Network to DataWeights and BiasesActivation Functions in the Hidden LayersThe Final LayerThe Training ProcessA Simple ModelFurther ReadingBackpropagation and Gradient DescentOverfittingRegularizationInterpretation and Variable ImportanceHyperparameter OptimizationFurther ReadingLoss (Cost) FunctionsLoss Functions for RegressionLoss Functions for ClassificationPerformanceSummaryExplore Further with Generative AI
9. Deep Learning
Advantages of DepthRNNsText DataMemory IssuesFurther ReadingCNNsIntuition Behind CNNsCNN ArchitectureImage Data and Transfer LearningPutting It All Together: An ExampleFurther ReadingTransformersEmbeddingsAttention MechanismsArchitecture OverviewRevisiting the MNIST Image Classification ExampleFurther ReadingReinforcement LearningMulti-Arm BanditsMarkov Decision ProcessesDeep Reinforcement LearningFurther ReadingSummaryExplore Further with Generative AI

10. Generative AI and Large Language Models
Generative AI and LLMsPredicting the Next WordCreativity and TemperatureLearning Language and Generating Images: AttentionFurther ReadingReinforcement Learning From Human FeedbackAlignmentReward ModelsFurther ReadingImage GenerationIntegrating Text InputFurther ReadingScale and PowerParallelization and GPUsMixture of Experts ModelsEmergent CapabilitiesArtificial General IntelligenceFurther ReadingTraining LLMs and Adapting LLMs for Specific PurposesFine TuningRetrieval Augmented Generation (RAG)Web SearchesPrompt EngineeringPattern Matching and In-Context LearningInteractive ConversationFurther ReadingDeploying and Using Generative AIAugmenting AI Training DataIntegrating Generative AI Into ApplicationsAgentic AITask Decomposition and Chain-of-Thought ReasoningThe Generative AI StackFurther ReadingSummaryExplore Further with Generative AI
11. Caveats and Concerns
Accuracy and ReliabilityModel CollapseValue of Proprietary DataVibe CodingUsing AI for Statistical AnalysisMisinterpreting Visual InformationInconsistency and SloppinessArithmetic and Problem SolvingSelf-CriticismBest practicesAI, Society, and the IndividualPersonal CompanionsBig BrotherArtificial General IntelligenceWorld ModelsData Curation and GovernanceData Curation PipelineData Ownership and Control ProtectionSummaryExploration with AI
Bibliography

Content preview from AI-Assisted Statistics for Data Scientists, 3rd Edition

Bibliography

[Baumer-2017] Baumer, Benjamin, Daniel Kaplan, and Nicholas Horton. Modern Data Science with R. Boca Raton, Fla.: Chapman & Hall/CRC Press, 2017.
[bokeh] Bokeh Development Team. “Bokeh: Python library for interactive visualization” (2014). https://bokeh.pydata.org.
[Deng-Wickham-2011] Deng, Henry, and Hadley Wickham. “Density Estimation in R.” September 2011. https://oreil.ly/-Ny_6.
[Donoho-2015] Donoho, David. “50 Years of Data Science.” September 18, 2015. https://oreil.ly/kqFb0.
[Duong-2001] Duong, Tarn. “An Introduction to Kernel Density Estimation.” 2001. https://oreil.ly/Z5A7W.
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AI-Assisted Statistics for Data Scientists, 3rd Edition

by Peter Bruce, Andrew Bruce, Peter Gedeck

Bibliography

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,
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