Skip to main content
Ctrl+K
scikit-plots homepage scikit-plots homepage
  • Introduction
  • User Guide
  • APIs Reference
  • Tutorials
  • Learn
    • Tags
    • Code of Conduct
    • Community
    • Developer's Guide
    • Governance Process
    • Release History
    • Roadmap
    • About Us | Project
  • Social Media Platforms
  • PyPI
  • GitHub
  • Introduction
  • User Guide
  • APIs Reference
  • Tutorials
  • Learn
  • Tags
  • Code of Conduct
  • Community
  • Developer's Guide
  • Governance Process
  • Release History
  • Roadmap
  • About Us | Project
  • Social Media Platforms
  • PyPI
  • GitHub

Section Navigation

Tags

  • component: model (1)
    • plot_calibration with examples
  • domain: bayesian (145)
    • The three steps of Bayesian data analysis
    • General Notation for Statistical Inference
    • Bayesian Inference
    • Discrete Bayesian Examples – Genetics and Spell Checking (with θ)
    • Probability as a Measure of Uncertainty
    • Example — Probabilities from Football Point Spreads
    • Example — Calibration for Record Linkage
    • Some Useful Results from Probability Theory
    • Computation and Software
    • Bayesian Inference in Applied Statistics
    • Estimating a Probability from Binomial Data
    • Posterior as a Compromise Between Data and Prior Information
    • Summarizing Posterior Inference
    • Informative Prior Distributions
    • Normal Distribution with Known Variance
    • Other Standard Single-Parameter Models
    • Informative Prior Distribution for Cancer Rates
    • Noninformative Prior Distributions
    • Weakly Informative Prior Distributions
    • Averaging Over Nuisance Parameters
    • Normal Data with a Noninformative Prior Distribution
    • Normal Data with a Conjugate Prior Distribution
    • Multinomial Model for Categorical Data
    • Multivariate Normal Model with Known Variance
    • Multivariate Normal with Unknown Mean and Variance
    • Example: Bayesian analysis of a bioassay experiment (logistic, nonconjugate)
    • Summary of Elementary Modeling and Computation
    • Normal Approximations to the Posterior Distribution
    • Large-Sample Theory
    • Counterexamples to large-sample (asymptotic) Bayesian theorems
    • Frequency Evaluations of Bayesian Inferences
    • Bayesian interpretations of other statistical methods
    • Constructing a Parameterized Prior Distribution
    • Exchangeability and hierarchical models
    • Bayesian analysis of conjugate hierarchical models
    • Normal model with exchangeable parameters
    • Example: parallel experiments in eight schools
    • Hierarchical modeling applied to a meta-analysis
    • Weakly Informative Priors for Variance Parameters
    • The Place of Model Checking in Applied Bayesian Statistics
    • Do the Inferences from the Model Make Sense?
    • Posterior predictive checking
    • Graphical posterior predictive checks
    • Model checking for the educational testing example
    • Measures of predictive accuracy
    • Model comparison based on predictive performance
    • Model comparison using Bayes factors
    • Continuous model expansion
    • Implicit assumptions and model expansion: an example
    • Bayesian inference requires a model for data collection
    • Data-collection models and ignorability
    • Sample surveys
    • Designed experiments
    • Sensitivity and the role of randomization
    • Observational studies
    • Censoring and truncation
    • Bayesian decision theory in different contexts
    • Using regression predictions: survey incentives
    • Multistage decision making: medical screening
    • Hierarchical decision analysis for home radon
    • Personal vs. institutional decision analysis
    • Numerical integration
    • Distributional approximations
    • Direct simulation and rejection sampling
    • Importance sampling
    • How many simulation draws are needed?
    • Computing environments
    • Debugging Bayesian computing
    • Gibbs sampler
    • Metropolis and Metropolis-Hastings algorithms
    • Using Gibbs and Metropolis as building blocks
    • Inference and assessing convergence
    • Effective number of simulation draws
    • Example: hierarchical normal model
    • Efficient Gibbs samplers
    • Efficient Metropolis jumping rules
    • Further extensions to Gibbs and Metropolis
    • Hamiltonian Monte Carlo
    • Hamiltonian Monte Carlo for a hierarchical model
    • Stan: developing a computing environment
    • Finding posterior modes
    • Boundary-avoiding priors for modal summaries
    • Normal and related mixture approximations
    • Finding marginal posterior modes using EM
    • Conditional and marginal posterior approximations
    • Example: hierarchical normal model (continued)
    • Variational inference
    • Expectation propagation
    • Other approximations
    • Unknown normalizing factors
    • Conditional modeling
    • Bayesian analysis of classical regression
    • Regression for causal inference: incumbency and voting
    • Goals of regression analysis
    • Assembling the matrix of explanatory variables
    • Regularization and dimension reduction
    • Unequal variances and correlations
    • Including numerical prior information
    • Regression coefficients exchangeable in batches
    • Example: forecasting U.S. presidential elections
    • Interpreting a normal prior distribution as extra data
    • Varying intercepts and slopes
    • Computation: batching and transformation
    • Analysis of variance and the batching of coefficients
    • Hierarchical models for batches of variance components
    • Standard generalized linear model likelihoods
    • Working with generalized linear models
    • Weakly informative priors for logistic regression
    • Overdispersed Poisson regression for police stops
    • State-level opinons from national polls
    • Models for multivariate and multinomial responses
    • Loglinear models for multivariate discrete data
    • Aspects of robustness
    • Overdispersed versions of standard models
    • Posterior inference and computation
    • Robust inference for the eight schools
    • Robust regression using t-distributed errors
    • Notation
    • Multiple imputation
    • Missing data in the multivariate normal and t models
    • Example: multiple imputation for a series of polls
    • Missing values with counted data
    • Example: an opinion poll in Slovenia
    • Example: serial dilution assay
    • Example: population toxicokinetics
    • Splines and weighted sums of basis functions
    • Basis selection and shrinkage of coefficients
    • Non-normal models and regression surfaces
    • Gaussian process regression
    • Example: birthdays and birthdates
    • Latent Gaussian process models
    • Functional data analysis
    • Density estimation and regression
    • Setting up and interpreting mixture models
    • Example: reaction times and schizophrenia
    • Label switching and posterior computation
    • Unspecified number of mixture components
    • Mixture models for classification and regression
    • Bayesian histograms
    • Dirichlet process prior distributions
    • Dirichlet process mixtures
    • Beyond density estimation
    • Hierarchical dependence
    • Density regression
    • Bayesian Data Analysis
  • domain: cython (11)
    • Cython quickstart: compile_and_load
    • Browse and compile templates
    • Build profiles: fast-debug, release, annotate
    • Cache and restart reuse
    • Pin/Alias: stable handles for cached builds
    • Multi-module package builds (5 package examples)
    • Multi-file builds: .pxi includes and external headers
    • C++ mode basics: cppclass and libcpp containers
    • Vector ops without NumPy: array(‘d’) + memoryviews
    • Workflow templates (train / hpo / predict) + CLI entry template
    • Cython: Realtime compile_and_load (.pyx)
  • domain: mlflow (1)
    • MLflow
  • domain: neural network (9)
    • Visualkeras: Spam Classification Conv1D Dense Example
    • visualkeras: Spam Dense example
    • visualkeras: autoencoder example
    • visualkeras: custom vgg16 example
    • visualkeras: custom vgg16 show dimension example
    • visualkeras: EfficientNetV2 example
    • visualkeras: ResNetV2 example
    • visualkeras: custom VGG example
    • visualkeras: Vector Index DB
  • domain: statistics (5)
    • plot_ks_statistic with examples
    • plot_lift with examples
    • plot_residuals_distribution with examples
    • Gaussian Mixture Models — AIC, AICc, and BIC Model Selection
    • plot_residuals_distribution with examples
  • internal: needs-review (1)
    • Tag Glossary
  • level: advanced (264)
    • Conditional modeling
    • Bayesian analysis of classical regression
    • Regression for causal inference: incumbency and voting
    • Goals of regression analysis
    • Assembling the matrix of explanatory variables
    • Regularization and dimension reduction
    • Unequal variances and correlations
    • Including numerical prior information
    • Regression coefficients exchangeable in batches
    • Example: forecasting U.S. presidential elections
    • Interpreting a normal prior distribution as extra data
    • Varying intercepts and slopes
    • Computation: batching and transformation
    • Analysis of variance and the batching of coefficients
    • Hierarchical models for batches of variance components
    • Standard generalized linear model likelihoods
    • Working with generalized linear models
    • Weakly informative priors for logistic regression
    • Overdispersed Poisson regression for police stops
    • State-level opinons from national polls
    • Models for multivariate and multinomial responses
    • Loglinear models for multivariate discrete data
    • Aspects of robustness
    • Overdispersed versions of standard models
    • Posterior inference and computation
    • Robust inference for the eight schools
    • Robust regression using t-distributed errors
    • Notation
    • Multiple imputation
    • Missing data in the multivariate normal and t models
    • Example: multiple imputation for a series of polls
    • Missing values with counted data
    • Example: an opinion poll in Slovenia
    • Example: serial dilution assay
    • Example: population toxicokinetics
    • Splines and weighted sums of basis functions
    • Basis selection and shrinkage of coefficients
    • Non-normal models and regression surfaces
    • Gaussian process regression
    • Example: birthdays and birthdates
    • Latent Gaussian process models
    • Functional data analysis
    • Density estimation and regression
    • Setting up and interpreting mixture models
    • Example: reaction times and schizophrenia
    • Label switching and posterior computation
    • Unspecified number of mixture components
    • Mixture models for classification and regression
    • Bayesian histograms
    • Dirichlet process prior distributions
    • Dirichlet process mixtures
    • Beyond density estimation
    • Hierarchical dependence
    • Density regression
    • Logistic Regression: Modeling Binary Outcomes via Odds and Log-Odds
    • Maximum Likelihood (MLE): Fitting a Distribution to Observed Data
    • Assessing Model Fit in Logistic Regression
    • Complete and Quasi-Complete Separation in Logistic Regression
    • Forward Selection with Nested Models and Deviance Tests
    • Interpreting and Assessing a Forward-Selection Logistic Regression Model for College Student Retention
    • Motivation of Decision Trees: An Incremental Model of Decision-Making
    • The CART Algorithm
    • Decision Trees as Piecewise Models and Their Predictive Structure
    • How CART Decision Trees Model Interactions
    • Cluster Profiling Using Decision Trees
    • Using Decision Trees to Explain Clustering Results
    • Assessing the Quality of Prediction Models
    • Binary Classification Models – Conceptual Framework and Evaluation Metrics
    • Nominal Classification Models: Model State and Evaluation Metrics
    • Binary Classification Model Evaluation and Threshold Optimization
    • Identifying Outliers Using Residuals and Studentized Residuals
    • AUC–ROC Curve: Evaluating Classification Model Performance
    • Lift Analysis for Direct Mail Campaigns: Concept, Process, and Business Value
    • Low-pass Filtering
    • Signal Processing
    • Time Series
    • Predictive Parity (Calibration)
    • Equalized Odds (Fairness)
    • Equal Opportunity (Fairness)
    • Demographic Parity (Statistical Parity)
    • Thompson Sampling (TS) in Bandits (Multi-Armed Bandit Problem (MAB))
    • Bayesian Decision Theory (BDT)
    • Bayesian Time Series
    • Posterior probability of uplift
    • Gaussian Processes (GPs)
    • Bayesian Neural Networks (BNNs)
    • Variational Inference (VI)
    • MCMC (Markov Chain Monte Carlo)
    • Sequential Settings
    • Binomial Likelihood
    • Posterior belief
    • Marginal Likelihood (also called The Model Evidence or Integrated Likelihood)
    • Posterior
    • Prior Belief (or Prior Probability)
    • Parameter(s) of Interest
    • Bayes’ Theorem
    • Posterior Probability
    • Sequential Probability Ratio Test (SPRT)
    • Pocock Method
    • O’Brien–Fleming (OBF) Method
    • Ranking Algorithms
    • Probabilistic Interleaving
    • Team Draft Interleaving (TDI)
    • Balanced Interleaving
    • Causal Impact
    • Bandit Algorithms
    • Causal Inference
    • Temporal autocorrelation (Serial Correlation)
    • Re-scoring
    • Drift Detection
    • AWS SageMaker Endpoints
    • Cloud Inference with Big Payloads
    • Cloud Inference
    • Recalibration
    • Reweighting
    • Continuous Retraining
    • Monitoring Pipelines
    • Bayesian Correction
    • Recalibrate Thresholds
    • Guardrails (in ML & Data Systems)
    • Model KPIs (Key Performance Indicators)
    • Windows (in Time-Series)
    • Autoencoder
    • Frozen Encoder
    • Embedding
    • Representation Shift
    • Classifier Two-Sample Tests (C2STs)
    • Energy Distance
    • Maximum Mean Discrepancy (MMD)
    • Cardinality in Categorical Data
    • Categorical Drift
    • Macro Shifts
    • Categorical Explosions
    • Off-Distribution
    • Model Stability
    • Feature Values
    • Four-Fifths (80%) Rule
    • SLI (Service Level Indicator)
    • Treatment Cost
    • Incremental Revenue
    • Incremental Recovery Rate (IRR)
    • Incremental Sales
    • Random Targeting Strategy
    • Causal ML (Causal Machine Learning)
    • Cumulative Uplift
    • Incremental Gain
    • Total Incremental Benefit (TIB)
    • Cumulative Incremental Gain (CIG)
    • Qini Curve
    • Uplift Score
    • Uplift Models
    • Ops Health Dashboard
    • SLA Breach Rate
    • SLA (Service Level Agreement)
    • Prophet — Time Series Forecasting by Facebook (Meta)
    • LSTM — Long Short-Term Memory Networks
    • ARIMA (AutoRegressive Integrated Moving Average)
    • Return Distribution
    • Value-at-Risk (VaR)
    • Risk Forecast
    • Probabilistic Scoring
    • Full Distribution
    • Continuous Probabilistic Forecasts
    • Quantile Forecasts
    • Point Forecasts
    • Strictly Proper Scoring Rules
    • Probability Forecasts
    • Probabilistic Forecasts
    • Deterministic forecasts
    • M-Competitions (Makridakis Competitions)
    • Forecasting Benchmarks
    • Seasonal Lag
    • Simple Baseline Methods
    • Naïve Baseline Forecast
    • Forecast Error
    • Forecasting Competitions
    • Predicting Percentiles
    • Prediction Intervals (PI)
    • Quantile Regression
    • Quantile Level
    • Time Series Forecasting
    • Log-Space
    • Self-Information of Popularity
    • Relevance in Recommender Systems
    • Genre Overlap
    • Jaccard index
    • Cosine Similarity of Item Features
    • Intra-List Diversity (ILD)
    • Dominating in Recommender Systems
    • Catalog Coverage
    • User Coverage
    • Item Coverage
    • Diminishing Utility
    • DCG (Discounted Cumulative Gain)
    • TREC (Text REtrieval Conference)
    • Adaptive ECE (Expected Calibration Error with Adaptive Binning)
    • Maximum Calibration Error (MCE)
    • Murphy’s Decomposition
    • Temperature Scaling
    • Platt Scaling
    • Isotonic Regression
    • Underconfident
    • Overconfident
    • Risk-Based Decisions
    • Causal Trees
    • Uplift Random Forests
    • Uplift Curve
    • Causal Effect
    • Embedding Similarity
    • Jensen–Shannon (JS) Divergence
    • Kullback–Leibler (KL) Divergence
    • Seasonality
    • Concept Drift
    • Data Drift
    • Fair Lending laws
    • Basel III
    • High-Stakes Domains
    • Counterfactual Explanations
    • LIME (Local Interpretable Model-agnostic Explanations)
    • SHAP (SHapley Additive exPlanations)
    • Post-hoc Explainability
    • Caching
    • Drift Guardrails
    • Latency Guardrails
    • Fairness Guardrails
    • Dataset Shift
    • Fairness parity
    • Bayesian Inference.
    • Interleaving Tests
    • Compute budgets
    • Manual review minutes
    • Inference Cost (Inference $)
    • Label Drift (a.k.a. Target Drift)
    • Covariate Drift (a.k.a. Covariate Shift)
    • KS shift (Kolmogorov–Smirnov shift)
    • PSI (Population Stability Index)
    • Selection Rate
    • SLOs (Service Level Objectives)
    • Revenue net of treatment cost
    • Incremental Conversions
    • Uplift@k
    • AUUC (Area Under the Uplift Curve)
    • Qini Coefficient
    • SLA Breaches
    • Continuous Ranked Probability Score (CRPS)
    • Pinball Loss (a.k.a. Quantile Loss)
    • Novelty (in Recommender Systems)
    • Diversity (in Recommender Systems)
    • Hit Rate (HR)
    • NDCG (Normalized Discounted Cumulative Gain)
    • Mean Average Precision (MAP)
    • Expected Calibration Error (ECE)
    • Reliability Curves (also called Calibration Curves)
    • Brier Score
    • Calibration quality (Model Calibration)
    • Uplift
    • Preliminary Estimation for AR Models and the Yule–Walker Equations
    • Maximum Likelihood Estimation for ARMA Models (Gaussian MLE)
    • Diagnostics After Fitting a Time Series Model
    • Order Selection for Time Series Models
    • ARIMA Models: How Nonstationary Models Are Built from Stationary Ones
    • SARIMA Models: Seasonal ARIMA
    • Beyond One-Step Ahead Predictions
    • Exponential Smoothing Models
  • level: beginner (177)
    • annoy.Annoy legacy c-api with examples
    • annoy.Index python-api with examples
    • Index (cython) python-api benchmark with examples
    • Index (cython) python-api with examples
    • Approximate Nearest Neighbors with Annoy — A Hamlet Example
    • annoy.Index to NPY or CSV with examples
    • Mmap annoy.AnnoyIndex with examples
    • Precision annoy.AnnoyIndex with examples
    • Simple annoy.AnnoyIndex with examples
    • plot_calibration with examples
    • plot_classifier_eval with examples
    • plot_confusion_matrix with examples
    • plot_feature_importances with examples
    • plot_learning_curve with examples
    • plot_precision_recall with examples
    • plot_roc_curve with examples
    • plot_elbow with examples
    • plot_silhouette with examples
    • corpus A Tale of Two Cities .mp3 with examples
    • corpus Knowledge and Information local .png with examples
    • corpus WHO European Region local or url per file with examples
    • corpus WHO European Region YouTube shorts with examples
    • corpus WHO European Region local .zip with examples
    • Cython: Realtime compile_and_load (.pyx)
    • plot_cumulative_gain with examples
    • plot_ks_statistic with examples
    • plot_lift with examples
    • Introduction to modelplotpy (legacy)
    • Introduction to modelplotpy
    • plot_report with examples
    • plot_pca_2d_projection with examples
    • plot_pca_component_variance with examples
    • annoy impute with examples
    • Memory-Mapping Showcase – Basic / Medium / Advanced
    • Misc Showcase
    • MLflow
    • plot_aucplot_script with examples
    • plot_decileplot_script with examples
    • plot_evalplot_script with examples
    • Gaussian Mixture Models — AIC, AICc, and BIC Model Selection
    • visualkeras: Spam Dense example
    • visualkeras: autoencoder example
    • visualkeras: EfficientNetV2 example
    • visualkeras: ResNetV2 example
    • visualkeras: custom VGG example
    • visualkeras: Vector Index DB
    • The three steps of Bayesian data analysis
    • General Notation for Statistical Inference
    • Bayesian Inference
    • Discrete Bayesian Examples – Genetics and Spell Checking (with θ)
    • Probability as a Measure of Uncertainty
    • Example — Probabilities from Football Point Spreads
    • Example — Calibration for Record Linkage
    • Some Useful Results from Probability Theory
    • Computation and Software
    • Bayesian Inference in Applied Statistics
    • Estimating a Probability from Binomial Data
    • Posterior as a Compromise Between Data and Prior Information
    • Summarizing Posterior Inference
    • Informative Prior Distributions
    • Normal Distribution with Known Variance
    • Other Standard Single-Parameter Models
    • Informative Prior Distribution for Cancer Rates
    • Noninformative Prior Distributions
    • Weakly Informative Prior Distributions
    • Averaging Over Nuisance Parameters
    • Normal Data with a Noninformative Prior Distribution
    • Normal Data with a Conjugate Prior Distribution
    • Multinomial Model for Categorical Data
    • Multivariate Normal Model with Known Variance
    • Multivariate Normal with Unknown Mean and Variance
    • Example: Bayesian analysis of a bioassay experiment (logistic, nonconjugate)
    • Summary of Elementary Modeling and Computation
    • Normal Approximations to the Posterior Distribution
    • Large-Sample Theory
    • Counterexamples to large-sample (asymptotic) Bayesian theorems
    • Frequency Evaluations of Bayesian Inferences
    • Bayesian interpretations of other statistical methods
    • Constructing a Parameterized Prior Distribution
    • Exchangeability and hierarchical models
    • Bayesian analysis of conjugate hierarchical models
    • Normal model with exchangeable parameters
    • Example: parallel experiments in eight schools
    • Hierarchical modeling applied to a meta-analysis
    • Weakly Informative Priors for Variance Parameters
    • Why Do We Analyze Data?
    • The Process of Data Analysis
    • CRISP-DM for Data Science
    • Big Data: Definition, Characteristics, Evolution, and Business Impact
    • The First Step in Knowing Your Data
    • IEEE 754 Floating-Point Standard
    • Discovering Associations Through Data: From Everyday Patterns to Chicago Taxi Trips (September 2022)
    • Taxi Trips – 2022 dataset from the City of Chicago open data portal
    • Objective Selection of the Bin Width for a Time Histogram
    • Measuring Associations in Data
    • Measuring Associations Between Two Continuous Variables
    • Correlation Coefficients in Python (Pearson, Spearman, Kendall)
    • Karl Pearson
    • Harald Cramér
    • What Are Statistical Tests?
    • Eta Squared (η²): Effect Size in ANOVA
    • What is a Neural Network?
    • Supervised Learning and Neural Networks
    • Why Deep Learning is Taking Off
    • Geoffrey Hinton Interview
    • Binary Classification and Logistic Regression (Neural Network Basics)
    • Logistic Regression (Binary Classification Model)
    • Logistic Regression – Loss Function and Cost Function
    • Probability
    • Frequentist
    • Type I Error
    • Standard Error (SE)
    • True Mean (Population Mean)
    • Margin of Error (MoE)
    • Critical Value
    • Sample Standard Deviation
    • Sample Mean
    • Regression Coefficient
    • Proportion
    • True Population Parameter
    • Compromise Power Analysis
    • Post Hoc Power Analysis
    • A Priori Power Analysis
    • Statistical Significance
    • Z-Score
    • Two-Proportion Z-Test
    • Beta Distribution
    • Minimum Detectable Lift (MDL)
    • Trivial Effects
    • Sample size
    • Power (1 – β)
    • Significance Level (α)
    • Effect Size (δ)
    • Hypothesis Testing
    • P-Value (probability value)
    • Z-Test
    • T-Test
    • Statistically Significant
    • IID (Independent and Identically Distributed)
    • AI (Artificial Intelligence)
    • Machine Learning (ML)
    • Medical AI
    • LLMs (Large Language Models)
    • Population Proportion
    • Target Variable
    • Probability Density
    • Normal Distribution
    • Probability Mass
    • Probability Distribution
    • Cumulative Distribution Function (CDF)
    • Support Vector Machines (SVMs)
    • Confidence Level
    • Neural Networks
    • Logistic Regression
    • Classification Models
    • Likelihood
    • Correlation
    • Outlier
    • Regression Models
    • Median
    • Mean
    • Computer Vision (CV)
    • Natural Language Processing (NLP)
    • Chi-square (χ²) Test
    • Kolmogorov–Smirnov (KS) Test
    • Statistical Tests
    • Decision Trees
    • Linear Models
    • Statistical Power
    • Clopper–Pearson Interval
    • Wilson Score Interval
    • Confidence Intervals (CIs)
    • Power Analysis
    • What Are Time Series, and How Are They Used?
    • Getting Started with R
    • A Gentle Introduction to Stationarity
    • Weak and Strong Stationarity
  • level: intermediate (276)
    • plot_residuals_distribution with examples
    • plot_residuals_distribution with examples
    • Visualkeras: Spam Classification Conv1D Dense Example
    • visualkeras: custom vgg16 example
    • visualkeras: custom vgg16 show dimension example
    • The Place of Model Checking in Applied Bayesian Statistics
    • Do the Inferences from the Model Make Sense?
    • Posterior predictive checking
    • Graphical posterior predictive checks
    • Model checking for the educational testing example
    • Measures of predictive accuracy
    • Model comparison based on predictive performance
    • Model comparison using Bayes factors
    • Continuous model expansion
    • Implicit assumptions and model expansion: an example
    • Bayesian inference requires a model for data collection
    • Data-collection models and ignorability
    • Sample surveys
    • Designed experiments
    • Sensitivity and the role of randomization
    • Observational studies
    • Censoring and truncation
    • Bayesian decision theory in different contexts
    • Using regression predictions: survey incentives
    • Multistage decision making: medical screening
    • Hierarchical decision analysis for home radon
    • Personal vs. institutional decision analysis
    • Numerical integration
    • Distributional approximations
    • Direct simulation and rejection sampling
    • Importance sampling
    • How many simulation draws are needed?
    • Computing environments
    • Debugging Bayesian computing
    • Gibbs sampler
    • Metropolis and Metropolis-Hastings algorithms
    • Using Gibbs and Metropolis as building blocks
    • Inference and assessing convergence
    • Effective number of simulation draws
    • Example: hierarchical normal model
    • Efficient Gibbs samplers
    • Efficient Metropolis jumping rules
    • Further extensions to Gibbs and Metropolis
    • Hamiltonian Monte Carlo
    • Hamiltonian Monte Carlo for a hierarchical model
    • Stan: developing a computing environment
    • Finding posterior modes
    • Boundary-avoiding priors for modal summaries
    • Normal and related mixture approximations
    • Finding marginal posterior modes using EM
    • Conditional and marginal posterior approximations
    • Example: hierarchical normal model (continued)
    • Variational inference
    • Expectation propagation
    • Other approximations
    • Unknown normalizing factors
    • Understanding Market Baskets and Ideal Customers
    • What Can Association Rules Tell Us?
    • How Association Rules Are Discovered: Concepts, Scale, Measures, and the Apriori Approach
    • Apriori: Frequent Itemsets via the Apriori Algorithm
    • association_rules: Generating Association Rules from Frequent Itemsets (mlxtend)
    • Cross-Selling
    • Stratified Random Sampling
    • Linear Congruential Random Number Generator (LCG)
    • Partitioning Observations to Train Objective Models
    • Putting Similar Observations into Clusters
    • Clustering
    • Recency, Frequency, and Monetary Value (RFM)
    • RFM Analysis
    • Creating Segments of Observations for Business Reasons (RFM)
    • Least Squares Regression
    • Multiple Linear Regression
    • Feature Importance in Linear Regression
    • Forward Selection: Definition and Core Idea
    • Forward Selection and Model Interpretation in Linear Regression
    • Understanding Forward and Backward Stepwise Regression
    • How Shapley Values Work
    • Gradient Descent in Logistic Regression
    • Derivatives
    • More Derivative Examples
    • Computation Graph
    • Derivatives with a Computation Graph
    • Logistic Regression Gradient Descent
    • Gradient Descent on m Training Examples
    • Vectorization in Logistic Regression
    • More Vectorization Examples
    • Vectorizing Logistic Regression
    • Subsampling
    • Class Weighting
    • SMOTE (Synthetic Minority Over-sampling Technique)
    • Oversampling
    • NearMiss (Distance-based Undersampling)
    • Cluster-based undersampling
    • Random Undersampling
    • Micro AUROC
    • Multi-label Classification
    • Micro F1
    • Single-label Classification
    • Micro Recall
    • Micro Precision
    • One-vs-Rest (OvR) AUROC
    • Macro AUROC (Macro-Averaged AUROC)
    • Macro F1
    • Macro Recall
    • Macro Precision
    • Multiclass AUROC
    • Gini Coefficient
    • Bootstrap Confidence Intervals (CIs)
    • Mann–Whitney U Test (also called the Wilcoxon rank-sum test)
    • Cross-Selling
    • Upselling
    • Customer Segmentation
    • SaaS (Software as a Service)
    • Valuation Metric
    • D2C (Direct-to-Consumer)
    • LTV:CAC Ratio
    • Net LTV (sometimes called Contribution LTV)
    • Gross LTV (Customer Lifetime Value)
    • Predictive LTV (pLTV)
    • Cohort-Based LTV (Simple Version)
    • Customer Lifetime
    • Gross Margin
    • Fully Loaded CAC (Customer Acquisition Cost)
    • Organic CAC (Customer Acquisition Cost)
    • Paid CAC (Customer Acquisition Cost)
    • Channel-Specific CAC (Customer Acquisition Cost)
    • Blended CAC (Customer Acquisition Cost)
    • Lead-Gen Software
    • Conversion Rate Uplift
    • Bayesian Stopping Rules
    • Optimizely
    • Online Experimentation Platforms
    • Stopping Rules
    • Treatment Effect
    • Bayesian Sequential Testing
    • Likelihood Ratio (LR)
    • Group Sequential Testing
    • Traditional A/B Test (Fixed-Horizon A/B Test)
    • Fixed-Horizon Testing
    • True Conversion Rate
    • Google Experiments
    • A/B/n Test
    • Multivariate Test (MVT)
    • Risk of Peeking
    • Session Length
    • Revenue per User (RPU / ARPU)
    • Churn
    • Retention
    • Blocked Splits (Single Holdout)
    • Sliding Window (Rolling Window) Cross-Validation
    • Expanding Window Cross-Validation
    • Data Leakage
    • Stratified Group K-Fold
    • Stratified Shuffle Split
    • Multiclass stratified CV
    • k-fold cross-validation
    • Cross-Validation (CV)
    • Model Distillation (Knowledge Distillation)
    • Early Stopping
    • Epochs
    • Hyperparameter
    • KYC
    • FTEs
    • AWS SageMaker
    • Vertex AI
    • OpenAI API (ML API)
    • Ensemble
    • Model Weights
    • FLOPs
    • OpEx
    • Active Learning
    • Lagging Indicators
    • Leading Indicators
    • Cramér’s V
    • Cohort
    • Discriminatory Power
    • KS Statistic (Kolmogorov–Smirnov Statistic)
    • ROI (Return on Investment)
    • Supplier Constraints
    • Long Lead Times
    • Slow-Moving SKUs
    • SKU
    • Real-Time Inventory Tracking
    • Supplier Management
    • Demand Forecasting
    • Reorder Point (ROP) Optimization
    • Safety Stock
    • Backorder Rate
    • Lost Sales Value
    • Fill Rate
    • Stockout Rate
    • Classification Probability
    • Average Absolute Error (AAE)
    • Relative accuracy
    • R² (R-squared)
    • Long-Tail Items
    • Kaggle
    • ROC Curve (Receiver Operating Characteristic)
    • Binary Cross-Entropy (BCE)
    • Loss Functions
    • Underflow
    • Logit Space
    • Binary Classification
    • Log-Odds
    • Softmax Function
    • Sigmoid Function
    • Squashing Function
    • Conversion Rate (CR)
    • Cost-Per-Click (CPC) Models
    • Mean Squared Error (MSE)
    • One-vs-Rest (OvR)
    • Multiclass Classification
    • Partial AUC (pAUC)
    • Micro AUC
    • Macro AUC
    • Sensitivity in Feature Engineering
    • Encode (in Feature Engineering)
    • Normalize (in Feature Engineering)
    • Accuracy
    • Deep Ensembles
    • Quantization
    • ONNX (Open Neural Network Exchange)
    • Full Annotation
    • Weak Supervision
    • TPU Clusters
    • DeLong’s Test
    • Label Noise
    • Evaluation Set
    • Per-class Precision (sometimes called class-wise precision)
    • Multiclass Precision
    • Multilabel Precision
    • Weighted Averaging
    • Harmonic Mean
    • F1-score
    • Model Score
    • Bootstrap
    • Average Precision (AP)
    • Upsampling
    • Downsampling
    • Micro Averaging
    • Macro Averaging
    • AUC (Area Under the Curve)
    • LTV (Customer Lifetime Value)
    • CAC (Customer Acquisition Cost)
    • Sequential Testing (also called sequential analysis)
    • A/B Testing
    • Time-based splits (a.k.a. Temporal Cross-Validation, Rolling Window Validation)
    • k-fold Stratified Cross-Validation (Stratified CV)
    • Cannibalization
    • Crew Overtime
    • Overstock %
    • Stockouts
    • MASE (Mean Absolute Scaled Error)
    • WMAPE (Weighted Mean Absolute Percentage Error)
    • sMAPE (Symmetric Mean Absolute Percentage Error)
    • RMSLE (Root Mean Squared Logarithmic Error)
    • Mean Absolute Error (MAE)
    • Coverage
    • Log Loss (also called Logarithmic Loss or Cross-Entropy Loss)
    • Logits
    • CTR (Click-Through Rate)
    • WAPE (Weighted Absolute Percentage Error)
    • Recall
    • Mean Absolute Percentage Error (MAPE)
    • Root Mean Squared Error (RMSE)
    • ROC-AUC (Receiver Operating Characteristic – Area Under Curve, = AUROC)
    • Baseline Heuristics
    • Precision (a.k.a. Positive Predictive Value, PPV)
    • Precision–Recall AUC (PR-AUC)
    • Advanced Sorting in Spreadsheets
    • Linear Processes
    • Understanding ARMA Processes
    • Computing ACFs of Causal AR(2) Processes Using Difference Equations
    • Understanding ACFs via Difference Equations for AR(p) and ARMA(p, q)
    • Best Linear Predictor of a Stationary Process
    • Sample ACF and Sample PACF
  • model-type: classification (35)
    • plot_calibration with examples
    • plot_classifier_eval with examples
    • plot_confusion_matrix with examples
    • plot_feature_importances with examples
    • plot_learning_curve with examples
    • plot_precision_recall with examples
    • plot_roc_curve with examples
    • corpus A Tale of Two Cities .mp3 with examples
    • corpus Knowledge and Information local .png with examples
    • corpus WHO European Region local or url per file with examples
    • corpus WHO European Region YouTube shorts with examples
    • corpus WHO European Region local .zip with examples
    • Cython: Realtime compile_and_load (.pyx)
    • plot_cumulative_gain with examples
    • plot_ks_statistic with examples
    • plot_lift with examples
    • Introduction to modelplotpy (legacy)
    • Introduction to modelplotpy
    • plot_report with examples
    • plot_pca_2d_projection with examples
    • plot_pca_component_variance with examples
    • annoy impute with examples
    • MLflow
    • plot_aucplot_script with examples
    • plot_decileplot_script with examples
    • plot_evalplot_script with examples
    • Visualkeras: Spam Classification Conv1D Dense Example
    • visualkeras: Spam Dense example
    • visualkeras: autoencoder example
    • visualkeras: custom vgg16 example
    • visualkeras: custom vgg16 show dimension example
    • visualkeras: EfficientNetV2 example
    • visualkeras: ResNetV2 example
    • visualkeras: custom VGG example
    • visualkeras: Vector Index DB
  • model-type: clustering (3)
    • plot_elbow with examples
    • plot_silhouette with examples
    • Gaussian Mixture Models — AIC, AICc, and BIC Model Selection
  • model-type: regression (3)
    • plot_pca_component_variance with examples
    • plot_residuals_distribution with examples
    • plot_residuals_distribution with examples
  • model-workflow: corpus (5)
    • corpus A Tale of Two Cities .mp3 with examples
    • corpus Knowledge and Information local .png with examples
    • corpus WHO European Region local or url per file with examples
    • corpus WHO European Region YouTube shorts with examples
    • corpus WHO European Region local .zip with examples
  • model-workflow: feature engineering (2)
    • plot_pca_2d_projection with examples
    • plot_pca_component_variance with examples
  • model-workflow: impute (1)
    • annoy impute with examples
  • model-workflow: model building (11)
    • Cython: Realtime compile_and_load (.pyx)
    • MLflow
    • Visualkeras: Spam Classification Conv1D Dense Example
    • visualkeras: Spam Dense example
    • visualkeras: autoencoder example
    • visualkeras: custom vgg16 example
    • visualkeras: custom vgg16 show dimension example
    • visualkeras: EfficientNetV2 example
    • visualkeras: ResNetV2 example
    • visualkeras: custom VGG example
    • visualkeras: Vector Index DB
  • model-workflow: model evaluation (20)
    • plot_calibration with examples
    • plot_classifier_eval with examples
    • plot_confusion_matrix with examples
    • plot_feature_importances with examples
    • plot_learning_curve with examples
    • plot_precision_recall with examples
    • plot_roc_curve with examples
    • plot_elbow with examples
    • plot_silhouette with examples
    • plot_cumulative_gain with examples
    • plot_ks_statistic with examples
    • plot_lift with examples
    • Introduction to modelplotpy (legacy)
    • Introduction to modelplotpy
    • plot_report with examples
    • plot_residuals_distribution with examples
    • plot_aucplot_script with examples
    • plot_decileplot_script with examples
    • plot_evalplot_script with examples
    • plot_residuals_distribution with examples
  • model-workflow: model-selection (1)
    • Gaussian Mixture Models — AIC, AICc, and BIC Model Selection
  • model-workflow: vector-db (9)
    • annoy.Annoy legacy c-api with examples
    • annoy.Index python-api with examples
    • Index (cython) python-api benchmark with examples
    • Index (cython) python-api with examples
    • Approximate Nearest Neighbors with Annoy — A Hamlet Example
    • annoy.Index to NPY or CSV with examples
    • Mmap annoy.AnnoyIndex with examples
    • Precision annoy.AnnoyIndex with examples
    • Simple annoy.AnnoyIndex with examples
  • plot-type: Inertia (sum of squared distances) (1)
    • plot_elbow with examples
  • plot-type: PCA (1)
    • plot_pca_2d_projection with examples
  • plot-type: WSS (within-cluster sum of squares) (1)
    • plot_elbow with examples
  • plot-type: auc (4)
    • plot_learning_curve with examples
    • plot_precision_recall with examples
    • plot_roc_curve with examples
    • plot_aucplot_script with examples
  • plot-type: bar (8)
    • Mmap annoy.AnnoyIndex with examples
    • Precision annoy.AnnoyIndex with examples
    • Simple annoy.AnnoyIndex with examples
    • plot_feature_importances with examples
    • plot_silhouette with examples
    • corpus WHO European Region local or url per file with examples
    • annoy impute with examples
    • Memory-Mapping Showcase – Basic / Medium / Advanced
  • plot-type: barh (1)
    • Misc Showcase
  • plot-type: cython (11)
    • Cython quickstart: compile_and_load
    • Browse and compile templates
    • Build profiles: fast-debug, release, annotate
    • Cache and restart reuse
    • Pin/Alias: stable handles for cached builds
    • Multi-module package builds (5 package examples)
    • Multi-file builds: .pxi includes and external headers
    • C++ mode basics: cppclass and libcpp containers
    • Vector ops without NumPy: array(‘d’) + memoryviews
    • Workflow templates (train / hpo / predict) + CLI entry template
    • Cython: Realtime compile_and_load (.pyx)
  • plot-type: decile (7)
    • plot_cumulative_gain with examples
    • plot_ks_statistic with examples
    • plot_lift with examples
    • Introduction to modelplotpy (legacy)
    • Introduction to modelplotpy
    • plot_report with examples
    • plot_decileplot_script with examples
  • plot-type: density (1)
    • Gaussian Mixture Models — AIC, AICc, and BIC Model Selection
  • plot-type: eval (4)
    • plot_classifier_eval with examples
    • plot_confusion_matrix with examples
    • plot_feature_importances with examples
    • plot_evalplot_script with examples
  • plot-type: heatmap (2)
    • plot_classifier_eval with examples
    • plot_confusion_matrix with examples
  • plot-type: histogram (2)
    • plot_residuals_distribution with examples
    • plot_residuals_distribution with examples
  • plot-type: line (16)
    • plot_calibration with examples
    • plot_learning_curve with examples
    • plot_precision_recall with examples
    • plot_roc_curve with examples
    • plot_elbow with examples
    • plot_cumulative_gain with examples
    • plot_ks_statistic with examples
    • plot_lift with examples
    • Introduction to modelplotpy (legacy)
    • Introduction to modelplotpy
    • plot_report with examples
    • plot_pca_component_variance with examples
    • plot_aucplot_script with examples
    • plot_decileplot_script with examples
    • plot_evalplot_script with examples
    • Gaussian Mixture Models — AIC, AICc, and BIC Model Selection
  • plot-type: model (1)
    • plot_calibration with examples
  • plot-type: qqplot (2)
    • plot_residuals_distribution with examples
    • plot_residuals_distribution with examples
  • plot-type: scatter (1)
    • plot_pca_2d_projection with examples
  • plot-type: silhouette (1)
    • plot_silhouette with examples
  • plot-type: text (6)
    • corpus A Tale of Two Cities .mp3 with examples
    • corpus Knowledge and Information local .png with examples
    • corpus WHO European Region YouTube shorts with examples
    • corpus WHO European Region local .zip with examples
    • Misc Showcase
    • MLflow
  • plot-type: visualkeras (9)
    • Visualkeras: Spam Classification Conv1D Dense Example
    • visualkeras: Spam Dense example
    • visualkeras: autoencoder example
    • visualkeras: custom vgg16 example
    • visualkeras: custom vgg16 show dimension example
    • visualkeras: EfficientNetV2 example
    • visualkeras: ResNetV2 example
    • visualkeras: custom VGG example
    • visualkeras: Vector Index DB
  • purpose: reference (897)
    • Documentation Tagging Guidelines
    • The three steps of Bayesian data analysis
    • General Notation for Statistical Inference
    • Bayesian Inference
    • Discrete Bayesian Examples – Genetics and Spell Checking (with θ)
    • Probability as a Measure of Uncertainty
    • Example — Probabilities from Football Point Spreads
    • Example — Calibration for Record Linkage
    • Some Useful Results from Probability Theory
    • Computation and Software
    • Bayesian Inference in Applied Statistics
    • Estimating a Probability from Binomial Data
    • Posterior as a Compromise Between Data and Prior Information
    • Summarizing Posterior Inference
    • Informative Prior Distributions
    • Normal Distribution with Known Variance
    • Other Standard Single-Parameter Models
    • Informative Prior Distribution for Cancer Rates
    • Noninformative Prior Distributions
    • Weakly Informative Prior Distributions
    • Averaging Over Nuisance Parameters
    • Normal Data with a Noninformative Prior Distribution
    • Normal Data with a Conjugate Prior Distribution
    • Multinomial Model for Categorical Data
    • Multivariate Normal Model with Known Variance
    • Multivariate Normal with Unknown Mean and Variance
    • Example: Bayesian analysis of a bioassay experiment (logistic, nonconjugate)
    • Summary of Elementary Modeling and Computation
    • Normal Approximations to the Posterior Distribution
    • Large-Sample Theory
    • Counterexamples to large-sample (asymptotic) Bayesian theorems
    • Frequency Evaluations of Bayesian Inferences
    • Bayesian interpretations of other statistical methods
    • Constructing a Parameterized Prior Distribution
    • Exchangeability and hierarchical models
    • Bayesian analysis of conjugate hierarchical models
    • Normal model with exchangeable parameters
    • Example: parallel experiments in eight schools
    • Hierarchical modeling applied to a meta-analysis
    • Weakly Informative Priors for Variance Parameters
    • The Place of Model Checking in Applied Bayesian Statistics
    • Do the Inferences from the Model Make Sense?
    • Posterior predictive checking
    • Graphical posterior predictive checks
    • Model checking for the educational testing example
    • Measures of predictive accuracy
    • Model comparison based on predictive performance
    • Model comparison using Bayes factors
    • Continuous model expansion
    • Implicit assumptions and model expansion: an example
    • Bayesian inference requires a model for data collection
    • Data-collection models and ignorability
    • Sample surveys
    • Designed experiments
    • Sensitivity and the role of randomization
    • Observational studies
    • Censoring and truncation
    • Bayesian decision theory in different contexts
    • Using regression predictions: survey incentives
    • Multistage decision making: medical screening
    • Hierarchical decision analysis for home radon
    • Personal vs. institutional decision analysis
    • Numerical integration
    • Distributional approximations
    • Direct simulation and rejection sampling
    • Importance sampling
    • How many simulation draws are needed?
    • Computing environments
    • Debugging Bayesian computing
    • Gibbs sampler
    • Metropolis and Metropolis-Hastings algorithms
    • Using Gibbs and Metropolis as building blocks
    • Inference and assessing convergence
    • Effective number of simulation draws
    • Example: hierarchical normal model
    • Efficient Gibbs samplers
    • Efficient Metropolis jumping rules
    • Further extensions to Gibbs and Metropolis
    • Hamiltonian Monte Carlo
    • Hamiltonian Monte Carlo for a hierarchical model
    • Stan: developing a computing environment
    • Finding posterior modes
    • Boundary-avoiding priors for modal summaries
    • Normal and related mixture approximations
    • Finding marginal posterior modes using EM
    • Conditional and marginal posterior approximations
    • Example: hierarchical normal model (continued)
    • Variational inference
    • Expectation propagation
    • Other approximations
    • Unknown normalizing factors
    • Conditional modeling
    • Bayesian analysis of classical regression
    • Regression for causal inference: incumbency and voting
    • Goals of regression analysis
    • Assembling the matrix of explanatory variables
    • Regularization and dimension reduction
    • Unequal variances and correlations
    • Including numerical prior information
    • Regression coefficients exchangeable in batches
    • Example: forecasting U.S. presidential elections
    • Interpreting a normal prior distribution as extra data
    • Varying intercepts and slopes
    • Computation: batching and transformation
    • Analysis of variance and the batching of coefficients
    • Hierarchical models for batches of variance components
    • Standard generalized linear model likelihoods
    • Working with generalized linear models
    • Weakly informative priors for logistic regression
    • Overdispersed Poisson regression for police stops
    • State-level opinons from national polls
    • Models for multivariate and multinomial responses
    • Loglinear models for multivariate discrete data
    • Aspects of robustness
    • Overdispersed versions of standard models
    • Posterior inference and computation
    • Robust inference for the eight schools
    • Robust regression using t-distributed errors
    • Notation
    • Multiple imputation
    • Missing data in the multivariate normal and t models
    • Example: multiple imputation for a series of polls
    • Missing values with counted data
    • Example: an opinion poll in Slovenia
    • Example: serial dilution assay
    • Example: population toxicokinetics
    • Splines and weighted sums of basis functions
    • Basis selection and shrinkage of coefficients
    • Non-normal models and regression surfaces
    • Gaussian process regression
    • Example: birthdays and birthdates
    • Latent Gaussian process models
    • Functional data analysis
    • Density estimation and regression
    • Setting up and interpreting mixture models
    • Example: reaction times and schizophrenia
    • Label switching and posterior computation
    • Unspecified number of mixture components
    • Mixture models for classification and regression
    • Bayesian histograms
    • Dirichlet process prior distributions
    • Dirichlet process mixtures
    • Beyond density estimation
    • Hierarchical dependence
    • Density regression
    • Bayesian Data Analysis
    • Why Data Analytics Matters Today
    • How Data Analytics Improves the Workplace
    • Data-Driven Decision-Making
    • Detectives and Data Analysts
    • The Six Phases of the Data Analysis Process
    • The Origins of Data Analysis and the Many Ways to Structure It
    • Understanding the Data Ecosystem
    • Understanding the Data Analysis Process and the Data Life Cycle
    • Understanding the Data Life Cycle
    • A Review of the Six Stages of the Data Life Cycle
    • The Stages of the Data Analysis Process and Their Roles
    • Practical Application of the Data Analysis Process
    • Analytical Skills and Their Core Components
    • Applying Analytical Skills in a Business Context
    • Analytical Thinking and Its Core Components
    • Analytical Thinking and Questions for Problem Solving
    • Root Cause Analysis and Business Applications of the Five Whys
    • Data-Driven Decision-Making and the Role of Analytical Skills
    • Case Studies in Data Analysis and the Practical Impact of Data-Driven Decision-Making
    • Overview of Core Tools Used by Data Analysts
    • The Role of Spreadsheets in Data Analysis and Basic Concepts
    • The Concept and Basic Use of SQL (Query Language)
    • The Role and Importance of Data Visualization
    • Industries Where Data Analysts Work and How Data Is Used
    • The Role of Business Tasks in Data Analysis
    • Fairness in Data Analysis
    • Key Factors to Consider When Choosing a Data Analytics Role
    • 🌱 Foundations
    • Using Data Analysis to Choose the Right Advertising Strategy
    • Understanding Common Problem Types in Data Analytics
    • Applying Data Analytics Problem Types in Real Business Scenarios
    • Why Asking the Right Questions Matters in Data Analytics
    • The Relationship Between Data and Decision-Making
    • Quantitative and Qualitative Data in Decision-Making
    • Data Creates Value Only When It Is Communicated
    • The Difference Between Data and Metrics, and the Role of Metrics
    • Dashboards
    • Mathematical Thinking
    • Spreadsheets in Data Analysis
    • Building and Organizing a Spreadsheet
    • How Data Analysts Use Spreadsheets
    • Spreadsheet Calculations with Formulas
    • Common Spreadsheet Errors and How to Fix Them
    • Spreadsheet Functions
    • Defining the Problem Domain
    • Context and Bias in Data Analysis
    • Stakeholder Expectations in Data Analysis
    • Staying Focused on the Project Objective
    • Clear Communication with Stakeholders and Teams
    • Adapting to Communication Expectations at Work
    • Managing Stakeholder Expectations and Project Constraints
    • Balancing Speed and Accuracy in Data Analysis
    • Sharing Data to Drive Impact
    • Effective Meetings
    • Conflict Resolution in the Workplace
    • 🎯 Data-Driven Decisions
    • How Data Is Generated and Collected
    • Choosing the Right Data to Collect
    • Understanding Data Types and Data Formats
    • Structured Data and Data Models
    • Data Types in Spreadsheets
    • Data Tables (Tabular Data)
    • Wide Data vs. Long Data
    • Understanding Bias in Data Analysis
    • Sampling Bias and Unbiased Data
    • Common Types of Data Bias
    • Identifying Good Data Sources (ROCCC Framework)
    • Identifying Bad Data Sources (When Data Does Not ROCCC)
    • Data Ethics in Data Analysis
    • Data Privacy in Data Ethics
    • Open Data and Openness in Data Ethics
    • Databases and Relational Database Concepts
    • Metadata in Databases
    • Metadata Repositories and Data Governance
    • Accessing Data: Internal and External Sources
    • Importing Data into Spreadsheets
    • Sorting and Filtering Data in Spreadsheets
    • BigQuery Account Types
    • Querying Data with SQL
    • Organizing Data for Personal and Work Projects
    • Data Security in Spreadsheets
    • 📦 Data Preparation
    • The Importance of Clean Data
    • Data Integrity and Its Risks in Data Analysis
    • Aligning Data with Business Objectives
    • Handling Insufficient Data in Data Analysis
    • Population, Sample Size, and Random Sampling
    • Statistical Power in Data Analysis
    • Sample Size and Data Integrity
    • Margin of Error
    • Dirty Data vs. Clean Data
    • The Importance of Clean Data (revisited)
    • Common Issues in Dirty Data
    • Data Cleaning with Spreadsheets
    • Cleaning and Merging Multiple Datasets
    • Spreadsheet Tools for Data Cleaning
    • Using Spreadsheet Functions for Data Cleaning
    • Viewing Data Differently for More Effective Data Cleaning
    • Data Mapping and the Big Picture of Clean Data
    • Introduction to SQL
    • Spreadsheets vs. SQL
    • Core SQL Queries for Data Cleaning and Analysis
    • Cleaning Data with SQL: Removing Duplicates and Cleaning String Variables
    • Using CAST to Clean and Format Data in SQL
    • Advanced SQL Functions for Data Cleaning
    • COALESCE
    • Verifying and Reporting Data Integrity
    • Verifying Data-Cleaning Efforts
    • Verification Techniques: Using Spreadsheets and SQL to Catch Repeated Errors
    • Documenting Data-Cleaning Changes
    • Reporting Data-Cleaning Results
    • Using Feedback from Data Cleaning to Improve Data Quality
    • Refining a Resume for Data Analytics Roles
    • Exploring Data Analyst Job Opportunities
    • 🧽 Data Cleaning & Preparation
    • Understanding Data Analysis
    • Data Organization in Analysis
    • Sorting and Filtering in Data Analysis
    • Sorting Data in Spreadsheets
    • Sorting and Filtering Data in SQL Using ORDER BY and WHERE
    • Data Formatting and Unit Conversion in Spreadsheets
    • Data Validation in Spreadsheets
    • Combining Data Validation and Conditional Formatting in Spreadsheets
    • Using CONCAT in SQL to Combine Text from Multiple Columns
    • Working with Strings in Spreadsheets (LEN, LEFT, RIGHT, FIND)
    • Problem-Solving and Seeking Help in Data Analysis
    • How to Effectively Search for Solutions Online as a Data Analyst
    • Choosing the Right Tool in Data Analysis
    • Preparing Data for VLOOKUP in Spreadsheets
    • Using VLOOKUP to Combine Data Across Spreadsheets
    • Troubleshooting VLOOKUP and Building a Problem-Solving Framework
    • Using JOIN in SQL to Combine Tables
    • Subqueries in SQL
    • Aggregating Data with Subqueries, HAVING, and CASE in SQL
    • Using Spreadsheet Formulas for Sales Trend Analysis
    • Using COUNTIF and SUMIF for Conditional Aggregation in Spreadsheets
    • Using SUMPRODUCT for Advanced Spreadsheet Calculations
    • Using Pivot Tables for Calculations and Trend Analysis
    • Using Pivot Table Filters and Calculated Fields for Deeper Analysis
    • Comparing Calculations in Spreadsheets and SQL
    • Embedding Calculations in SQL Queries
    • Using GROUP BY and ORDER BY for Aggregated Calculations in SQL
    • Data Validation as an Ongoing Analytical Process
    • Temporary Tables and the WITH Clause in SQL
    • Creating Temporary Tables in SQL — Methods, Trade-offs, and Best Practices
    • 📊 Analyze Data
    • Data Visualization
    • Connecting Data and Images
    • Creating Powerful Data Visualizations: Focus, Structure, and Analytical Purpose
    • Static vs. Dynamic Data Visualizations: Design Tradeoffs, Control, and Interactivity
    • Elements of Art in Data Visualization: Line, Shape, Color, Space, and Movement
    • Choosing the Right Visualization: Audience-Centered Design and Chart Selection
    • Design Thinking in Data Visualization: A User-Centered Framework
    • Accessibility in Data Visualization: Designing for Everyone
    • Introduction to Tableau
    • Getting Started with Tableau Public
    • Creating a CO₂ Emissions Visualization in Tableau Public
    • Effective vs. Ineffective Data Visualizations in Tableau
    • Using Creativity in Tableau
    • Linking Multiple Datasets in Tableau Public
    • Data Storytelling: Giving Numbers a Clear and Convincing Voice
    • Engaging Your Audience in Data Storytelling: Identifying the Key Message
    • Data Dashboards: Organizing Insight for Real-Time Decision Making
    • Using Filters to Create Compelling and Focused Visuals
    • Structuring a Persuasive Data Presentation: Turning Insights into Story
    • Designing Effective Data Presentation Slides: Structure, Visuals, and Professional Impact
    • Using a Strategic Framework to Structure Data Presentations
    • Weaving Data into Presentations: Hypotheses, Context, and the McCandless Method
    • Presentation Skills for Data Analysts: Delivering Insights with Confidence
    • Presenting Like a Pro: Best Practices for Data Analysts
    • Preparing for Q&A: Anticipating and Responding to Stakeholder Questions
    • Handling Objections in Data Presentations: Responding with Confidence and Clarity
    • Q&A Best Practices: Answering Questions with Clarity and Confidence
    • 🎨 Data Visualization
    • Introduction to Python and Programming Fundamentals
    • Python Fundamentals
    • Jupyter Notebook and Coding Environments
    • Object-Oriented Programming (OOP) in Python
    • Variables in Python
    • Naming Conventions and Restrictions in Python
    • Data Types and Type Conversion in Python
    • Functions in Python
    • Code Reusability, Modularity, and Clean Code in Python
    • Comments, Algorithms, and Docstrings in Python
    • Boolean Data, Comparators, and Logical Operators in Python
    • Branching and Conditional Statements in Python
    • While Loops and Iteration in Python
    • For Loops in Python
    • range() Function and Loop Control in Python
    • Strings in Python
    • String Indexing and Slicing in Python
    • String Formatting with .format() in Python
    • Data Types vs Data Structures & Introduction to Lists
    • Modifying Lists in Python
    • Tuples in Python
    • Advanced Use of Loops, Lists, Tuples & List Comprehension
    • Dictionaries in Python
    • Advanced Dictionary Usage in Python
    • Sets in Python
    • Libraries, Packages, and Modules in Python
    • Introduction to NumPy and Vectorization
    • NumPy Arrays (ndarray) and Core Concepts
    • Introduction to Pandas (Data Analysis Library)
    • Pandas DataFrame & Series
    • Boolean Masking in Pandas
    • Grouping and Aggregation in Pandas (groupby, agg)
    • Combining Data in Pandas (concat and merge)
    • 🐍 Data Analysis Using Python
    • Transferable Skills
    • Career Identity Statement
    • Career Dreamer (AI Tool for Career Exploration)
    • Job Search Plan (Using AI Tools)
    • Tailoring Your Resume
    • Using AI to Improve and Tailor Your Resume
    • Building a Professional Online Presence (Personal Brand)
    • Choosing the Right Job Platforms
    • Job Application Tracking (Using AI + Spreadsheets)
    • Networking for Job Search
    • Interview Preparation
    • STAR Method (Behavioral Interview)
    • Using AI (NotebookLM) for Interview Preparation
    • Practicing Interviews with AI (Gemini Live)
    • Post-Interview Strategy
    • 💼 Job Search
    • 📈 Data Analytics
    • Why Do We Analyze Data?
    • The Process of Data Analysis
    • CRISP-DM for Data Science
    • Big Data: Definition, Characteristics, Evolution, and Business Impact
    • The First Step in Knowing Your Data
    • IEEE 754 Floating-Point Standard
    • Discovering Associations Through Data: From Everyday Patterns to Chicago Taxi Trips (September 2022)
    • Taxi Trips – 2022 dataset from the City of Chicago open data portal
    • Objective Selection of the Bin Width for a Time Histogram
    • Measuring Associations in Data
    • Measuring Associations Between Two Continuous Variables
    • Correlation Coefficients in Python (Pearson, Spearman, Kendall)
    • Karl Pearson
    • Harald Cramér
    • What Are Statistical Tests?
    • Eta Squared (η²): Effect Size in ANOVA
    • Understanding Market Baskets and Ideal Customers
    • What Can Association Rules Tell Us?
    • How Association Rules Are Discovered: Concepts, Scale, Measures, and the Apriori Approach
    • Apriori: Frequent Itemsets via the Apriori Algorithm
    • association_rules: Generating Association Rules from Frequent Itemsets (mlxtend)
    • Cross-Selling
    • Stratified Random Sampling
    • Linear Congruential Random Number Generator (LCG)
    • Partitioning Observations to Train Objective Models
    • Putting Similar Observations into Clusters
    • Clustering
    • Recency, Frequency, and Monetary Value (RFM)
    • RFM Analysis
    • Creating Segments of Observations for Business Reasons (RFM)
    • Least Squares Regression
    • Multiple Linear Regression
    • Feature Importance in Linear Regression
    • Forward Selection: Definition and Core Idea
    • Forward Selection and Model Interpretation in Linear Regression
    • Understanding Forward and Backward Stepwise Regression
    • How Shapley Values Work
    • Logistic Regression: Modeling Binary Outcomes via Odds and Log-Odds
    • Maximum Likelihood (MLE): Fitting a Distribution to Observed Data
    • Assessing Model Fit in Logistic Regression
    • Complete and Quasi-Complete Separation in Logistic Regression
    • Forward Selection with Nested Models and Deviance Tests
    • Interpreting and Assessing a Forward-Selection Logistic Regression Model for College Student Retention
    • Motivation of Decision Trees: An Incremental Model of Decision-Making
    • The CART Algorithm
    • Decision Trees as Piecewise Models and Their Predictive Structure
    • How CART Decision Trees Model Interactions
    • Cluster Profiling Using Decision Trees
    • Using Decision Trees to Explain Clustering Results
    • Assessing the Quality of Prediction Models
    • Binary Classification Models – Conceptual Framework and Evaluation Metrics
    • Nominal Classification Models: Model State and Evaluation Metrics
    • Binary Classification Model Evaluation and Threshold Optimization
    • Identifying Outliers Using Residuals and Studentized Residuals
    • AUC–ROC Curve: Evaluating Classification Model Performance
    • Lift Analysis for Direct Mail Campaigns: Concept, Process, and Business Value
    • Data Preparation & Analysis
    • What is a Neural Network?
    • Supervised Learning and Neural Networks
    • Why Deep Learning is Taking Off
    • Geoffrey Hinton Interview
    • Binary Classification and Logistic Regression (Neural Network Basics)
    • Logistic Regression (Binary Classification Model)
    • Logistic Regression – Loss Function and Cost Function
    • Gradient Descent in Logistic Regression
    • Derivatives
    • More Derivative Examples
    • Computation Graph
    • Derivatives with a Computation Graph
    • Logistic Regression Gradient Descent
    • Gradient Descent on m Training Examples
    • Vectorization in Logistic Regression
    • More Vectorization Examples
    • Vectorizing Logistic Regression
    • Deep Learning
    • Subsampling
    • Class Weighting
    • SMOTE (Synthetic Minority Over-sampling Technique)
    • Oversampling
    • Low-pass Filtering
    • NearMiss (Distance-based Undersampling)
    • Cluster-based undersampling
    • Random Undersampling
    • Signal Processing
    • Time Series
    • Micro AUROC
    • Multi-label Classification
    • Micro F1
    • Single-label Classification
    • Micro Recall
    • Micro Precision
    • One-vs-Rest (OvR) AUROC
    • Macro AUROC (Macro-Averaged AUROC)
    • Macro F1
    • Macro Recall
    • Macro Precision
    • Multiclass AUROC
    • Gini Coefficient
    • Bootstrap Confidence Intervals (CIs)
    • Probability
    • Mann–Whitney U Test (also called the Wilcoxon rank-sum test)
    • Predictive Parity (Calibration)
    • Equalized Odds (Fairness)
    • Equal Opportunity (Fairness)
    • Demographic Parity (Statistical Parity)
    • Cross-Selling
    • Upselling
    • Customer Segmentation
    • SaaS (Software as a Service)
    • Valuation Metric
    • D2C (Direct-to-Consumer)
    • LTV:CAC Ratio
    • Net LTV (sometimes called Contribution LTV)
    • Gross LTV (Customer Lifetime Value)
    • Predictive LTV (pLTV)
    • Cohort-Based LTV (Simple Version)
    • Customer Lifetime
    • Gross Margin
    • Fully Loaded CAC (Customer Acquisition Cost)
    • Organic CAC (Customer Acquisition Cost)
    • Paid CAC (Customer Acquisition Cost)
    • Channel-Specific CAC (Customer Acquisition Cost)
    • Blended CAC (Customer Acquisition Cost)
    • Lead-Gen Software
    • Thompson Sampling (TS) in Bandits (Multi-Armed Bandit Problem (MAB))
    • Bayesian Decision Theory (BDT)
    • Bayesian Time Series
    • Posterior probability of uplift
    • Gaussian Processes (GPs)
    • Bayesian Neural Networks (BNNs)
    • Variational Inference (VI)
    • MCMC (Markov Chain Monte Carlo)
    • Sequential Settings
    • Frequentist
    • Binomial Likelihood
    • Posterior belief
    • Marginal Likelihood (also called The Model Evidence or Integrated Likelihood)
    • Posterior
    • Prior Belief (or Prior Probability)
    • Parameter(s) of Interest
    • Bayes’ Theorem
    • Conversion Rate Uplift
    • Bayesian Stopping Rules
    • Optimizely
    • Online Experimentation Platforms
    • Stopping Rules
    • Treatment Effect
    • Posterior Probability
    • Bayesian Sequential Testing
    • Likelihood Ratio (LR)
    • Sequential Probability Ratio Test (SPRT)
    • Pocock Method
    • O’Brien–Fleming (OBF) Method
    • Group Sequential Testing
    • Type I Error
    • Traditional A/B Test (Fixed-Horizon A/B Test)
    • Fixed-Horizon Testing
    • True Conversion Rate
    • Standard Error (SE)
    • True Mean (Population Mean)
    • Margin of Error (MoE)
    • Critical Value
    • Sample Standard Deviation
    • Sample Mean
    • Regression Coefficient
    • Proportion
    • True Population Parameter
    • Compromise Power Analysis
    • Post Hoc Power Analysis
    • A Priori Power Analysis
    • Statistical Significance
    • Z-Score
    • Two-Proportion Z-Test
    • Beta Distribution
    • Google Experiments
    • Minimum Detectable Lift (MDL)
    • Trivial Effects
    • Sample size
    • Power (1 – β)
    • Significance Level (α)
    • Effect Size (δ)
    • Hypothesis Testing
    • Ranking Algorithms
    • Probabilistic Interleaving
    • Team Draft Interleaving (TDI)
    • Balanced Interleaving
    • Causal Impact
    • Bandit Algorithms
    • A/B/n Test
    • Multivariate Test (MVT)
    • Risk of Peeking
    • Causal Inference
    • P-Value (probability value)
    • Z-Test
    • T-Test
    • Session Length
    • Revenue per User (RPU / ARPU)
    • Churn
    • Retention
    • Statistically Significant
    • IID (Independent and Identically Distributed)
    • Temporal autocorrelation (Serial Correlation)
    • Blocked Splits (Single Holdout)
    • Sliding Window (Rolling Window) Cross-Validation
    • Expanding Window Cross-Validation
    • Data Leakage
    • Stratified Group K-Fold
    • Stratified Shuffle Split
    • Multiclass stratified CV
    • k-fold cross-validation
    • Cross-Validation (CV)
    • Re-scoring
    • Drift Detection
    • Model Distillation (Knowledge Distillation)
    • Early Stopping
    • Epochs
    • Hyperparameter
    • AI (Artificial Intelligence)
    • Machine Learning (ML)
    • Medical AI
    • KYC
    • FTEs
    • AWS SageMaker
    • Vertex AI
    • OpenAI API (ML API)
    • AWS SageMaker Endpoints
    • Cloud Inference with Big Payloads
    • Cloud Inference
    • Ensemble
    • Model Weights
    • FLOPs
    • OpEx
    • LLMs (Large Language Models)
    • Recalibration
    • Reweighting
    • Continuous Retraining
    • Monitoring Pipelines
    • Active Learning
    • Bayesian Correction
    • Recalibrate Thresholds
    • Guardrails (in ML & Data Systems)
    • Model KPIs (Key Performance Indicators)
    • Lagging Indicators
    • Leading Indicators
    • Windows (in Time-Series)
    • Autoencoder
    • Frozen Encoder
    • Embedding
    • Representation Shift
    • Classifier Two-Sample Tests (C2STs)
    • Energy Distance
    • Maximum Mean Discrepancy (MMD)
    • Cardinality in Categorical Data
    • Categorical Drift
    • Cramér’s V
    • Macro Shifts
    • Categorical Explosions
    • Cohort
    • Off-Distribution
    • Discriminatory Power
    • KS Statistic (Kolmogorov–Smirnov Statistic)
    • Model Stability
    • Feature Values
    • Four-Fifths (80%) Rule
    • SLI (Service Level Indicator)
    • ROI (Return on Investment)
    • Treatment Cost
    • Incremental Revenue
    • Incremental Recovery Rate (IRR)
    • Incremental Sales
    • Random Targeting Strategy
    • Causal ML (Causal Machine Learning)
    • Cumulative Uplift
    • Population Proportion
    • Incremental Gain
    • Total Incremental Benefit (TIB)
    • Cumulative Incremental Gain (CIG)
    • Qini Curve
    • Uplift Score
    • Uplift Models
    • Ops Health Dashboard
    • SLA Breach Rate
    • SLA (Service Level Agreement)
    • Supplier Constraints
    • Long Lead Times
    • Slow-Moving SKUs
    • SKU
    • Real-Time Inventory Tracking
    • Supplier Management
    • Demand Forecasting
    • Reorder Point (ROP) Optimization
    • Safety Stock
    • Backorder Rate
    • Lost Sales Value
    • Fill Rate
    • Stockout Rate
    • Prophet — Time Series Forecasting by Facebook (Meta)
    • LSTM — Long Short-Term Memory Networks
    • ARIMA (AutoRegressive Integrated Moving Average)
    • Return Distribution
    • Value-at-Risk (VaR)
    • Risk Forecast
    • Probabilistic Scoring
    • Full Distribution
    • Continuous Probabilistic Forecasts
    • Classification Probability
    • Quantile Forecasts
    • Point Forecasts
    • Strictly Proper Scoring Rules
    • Probability Forecasts
    • Target Variable
    • Probability Density
    • Normal Distribution
    • Probability Mass
    • Probability Distribution
    • Probabilistic Forecasts
    • Deterministic forecasts
    • Cumulative Distribution Function (CDF)
    • M-Competitions (Makridakis Competitions)
    • Forecasting Benchmarks
    • Average Absolute Error (AAE)
    • Seasonal Lag
    • Simple Baseline Methods
    • Naïve Baseline Forecast
    • Forecast Error
    • Forecasting Competitions
    • Predicting Percentiles
    • Prediction Intervals (PI)
    • Quantile Regression
    • Quantile Level
    • Time Series Forecasting
    • Log-Space
    • Relative accuracy
    • R² (R-squared)
    • Long-Tail Items
    • Self-Information of Popularity
    • Relevance in Recommender Systems
    • Genre Overlap
    • Jaccard index
    • Cosine Similarity of Item Features
    • Intra-List Diversity (ILD)
    • Dominating in Recommender Systems
    • Catalog Coverage
    • User Coverage
    • Item Coverage
    • Diminishing Utility
    • DCG (Discounted Cumulative Gain)
    • Kaggle
    • TREC (Text REtrieval Conference)
    • Adaptive ECE (Expected Calibration Error with Adaptive Binning)
    • Maximum Calibration Error (MCE)
    • ROC Curve (Receiver Operating Characteristic)
    • Murphy’s Decomposition
    • Temperature Scaling
    • Platt Scaling
    • Isotonic Regression
    • Support Vector Machines (SVMs)
    • Underconfident
    • Overconfident
    • Confidence Level
    • Risk-Based Decisions
    • Neural Networks
    • Binary Cross-Entropy (BCE)
    • Loss Functions
    • Underflow
    • Logit Space
    • Logistic Regression
    • Binary Classification
    • Classification Models
    • Log-Odds
    • Softmax Function
    • Sigmoid Function
    • Squashing Function
    • Conversion Rate (CR)
    • Cost-Per-Click (CPC) Models
    • Causal Trees
    • Uplift Random Forests
    • Uplift Curve
    • Likelihood
    • Correlation
    • Causal Effect
    • Outlier
    • Mean Squared Error (MSE)
    • Regression Models
    • One-vs-Rest (OvR)
    • Multiclass Classification
    • Partial AUC (pAUC)
    • Micro AUC
    • Macro AUC
    • Median
    • Mean
    • Sensitivity in Feature Engineering
    • Encode (in Feature Engineering)
    • Normalize (in Feature Engineering)
    • Embedding Similarity
    • Computer Vision (CV)
    • Natural Language Processing (NLP)
    • Accuracy
    • Chi-square (χ²) Test
    • Kolmogorov–Smirnov (KS) Test
    • Jensen–Shannon (JS) Divergence
    • Kullback–Leibler (KL) Divergence
    • Statistical Tests
    • Seasonality
    • Concept Drift
    • Data Drift
    • Fair Lending laws
    • Basel III
    • High-Stakes Domains
    • Deep Ensembles
    • Counterfactual Explanations
    • LIME (Local Interpretable Model-agnostic Explanations)
    • SHAP (SHapley Additive exPlanations)
    • Post-hoc Explainability
    • Decision Trees
    • Linear Models
    • Caching
    • Quantization
    • ONNX (Open Neural Network Exchange)
    • Full Annotation
    • Weak Supervision
    • TPU Clusters
    • Statistical Power
    • Drift Guardrails
    • Latency Guardrails
    • Fairness Guardrails
    • DeLong’s Test
    • Dataset Shift
    • Label Noise
    • Evaluation Set
    • Clopper–Pearson Interval
    • Wilson Score Interval
    • Per-class Precision (sometimes called class-wise precision)
    • Multiclass Precision
    • Multilabel Precision
    • Weighted Averaging
    • Harmonic Mean
    • F1-score
    • Model Score
    • Bootstrap
    • Average Precision (AP)
    • Upsampling
    • Downsampling
    • Micro Averaging
    • Macro Averaging
    • AUC (Area Under the Curve)
    • Fairness parity
    • LTV (Customer Lifetime Value)
    • CAC (Customer Acquisition Cost)
    • Bayesian Inference.
    • Sequential Testing (also called sequential analysis)
    • Confidence Intervals (CIs)
    • Power Analysis
    • Interleaving Tests
    • A/B Testing
    • Time-based splits (a.k.a. Temporal Cross-Validation, Rolling Window Validation)
    • k-fold Stratified Cross-Validation (Stratified CV)
    • Compute budgets
    • Manual review minutes
    • Inference Cost (Inference $)
    • Label Drift (a.k.a. Target Drift)
    • Covariate Drift (a.k.a. Covariate Shift)
    • KS shift (Kolmogorov–Smirnov shift)
    • PSI (Population Stability Index)
    • Selection Rate
    • SLOs (Service Level Objectives)
    • Cannibalization
    • Revenue net of treatment cost
    • Incremental Conversions
    • Uplift@k
    • AUUC (Area Under the Uplift Curve)
    • Qini Coefficient
    • Crew Overtime
    • SLA Breaches
    • Overstock %
    • Stockouts
    • Continuous Ranked Probability Score (CRPS)
    • MASE (Mean Absolute Scaled Error)
    • Pinball Loss (a.k.a. Quantile Loss)
    • WMAPE (Weighted Mean Absolute Percentage Error)
    • sMAPE (Symmetric Mean Absolute Percentage Error)
    • RMSLE (Root Mean Squared Logarithmic Error)
    • Mean Absolute Error (MAE)
    • Novelty (in Recommender Systems)
    • Diversity (in Recommender Systems)
    • Coverage
    • Hit Rate (HR)
    • NDCG (Normalized Discounted Cumulative Gain)
    • Mean Average Precision (MAP)
    • Expected Calibration Error (ECE)
    • Reliability Curves (also called Calibration Curves)
    • Log Loss (also called Logarithmic Loss or Cross-Entropy Loss)
    • Brier Score
    • Calibration quality (Model Calibration)
    • Logits
    • CTR (Click-Through Rate)
    • WAPE (Weighted Absolute Percentage Error)
    • Recall
    • Uplift
    • Mean Absolute Percentage Error (MAPE)
    • Root Mean Squared Error (RMSE)
    • ROC-AUC (Receiver Operating Characteristic – Area Under Curve, = AUROC)
    • Baseline Heuristics
    • Precision (a.k.a. Positive Predictive Value, PPV)
    • Precision–Recall AUC (PR-AUC)
    • Advanced Sorting in Spreadsheets
    • Terminology
    • What Are Time Series, and How Are They Used?
    • Getting Started with R
    • A Gentle Introduction to Stationarity
    • Weak and Strong Stationarity
    • Linear Processes
    • Understanding ARMA Processes
    • Computing ACFs of Causal AR(2) Processes Using Difference Equations
    • Understanding ACFs via Difference Equations for AR(p) and ARMA(p, q)
    • Best Linear Predictor of a Stationary Process
    • Sample ACF and Sample PACF
    • Preliminary Estimation for AR Models and the Yule–Walker Equations
    • Maximum Likelihood Estimation for ARMA Models (Gaussian MLE)
    • Diagnostics After Fitting a Time Series Model
    • Order Selection for Time Series Models
    • ARIMA Models: How Nonstationary Models Are Built from Stationary Ones
    • SARIMA Models: Seasonal ARIMA
    • Beyond One-Step Ahead Predictions
    • Exponential Smoothing Models
    • Time Series
  • purpose: showcase (61)
    • annoy.Annoy legacy c-api with examples
    • annoy.Index python-api with examples
    • Index (cython) python-api benchmark with examples
    • Index (cython) python-api with examples
    • Approximate Nearest Neighbors with Annoy — A Hamlet Example
    • annoy.Index to NPY or CSV with examples
    • Mmap annoy.AnnoyIndex with examples
    • Precision annoy.AnnoyIndex with examples
    • Simple annoy.AnnoyIndex with examples
    • plot_calibration with examples
    • plot_classifier_eval with examples
    • plot_confusion_matrix with examples
    • plot_feature_importances with examples
    • plot_learning_curve with examples
    • plot_precision_recall with examples
    • plot_roc_curve with examples
    • plot_elbow with examples
    • plot_silhouette with examples
    • corpus A Tale of Two Cities .mp3 with examples
    • corpus Knowledge and Information local .png with examples
    • corpus WHO European Region local or url per file with examples
    • corpus WHO European Region YouTube shorts with examples
    • corpus WHO European Region local .zip with examples
    • Cython quickstart: compile_and_load
    • Browse and compile templates
    • Build profiles: fast-debug, release, annotate
    • Cache and restart reuse
    • Pin/Alias: stable handles for cached builds
    • Multi-module package builds (5 package examples)
    • Multi-file builds: .pxi includes and external headers
    • C++ mode basics: cppclass and libcpp containers
    • Vector ops without NumPy: array(‘d’) + memoryviews
    • Workflow templates (train / hpo / predict) + CLI entry template
    • Cython: Realtime compile_and_load (.pyx)
    • plot_cumulative_gain with examples
    • plot_ks_statistic with examples
    • plot_lift with examples
    • Introduction to modelplotpy (legacy)
    • Introduction to modelplotpy
    • plot_report with examples
    • plot_pca_2d_projection with examples
    • plot_pca_component_variance with examples
    • annoy impute with examples
    • Memory-Mapping Showcase – Basic / Medium / Advanced
    • Misc Showcase
    • MLflow
    • plot_residuals_distribution with examples
    • plot_aucplot_script with examples
    • plot_decileplot_script with examples
    • plot_evalplot_script with examples
    • Gaussian Mixture Models — AIC, AICc, and BIC Model Selection
    • plot_residuals_distribution with examples
    • Visualkeras: Spam Classification Conv1D Dense Example
    • visualkeras: Spam Dense example
    • visualkeras: autoencoder example
    • visualkeras: custom vgg16 example
    • visualkeras: custom vgg16 show dimension example
    • visualkeras: EfficientNetV2 example
    • visualkeras: ResNetV2 example
    • visualkeras: custom VGG example
    • visualkeras: Vector Index DB
  • topic: advanced (3)
    • Data Validation as an Ongoing Analytical Process
    • Temporary Tables and the WITH Clause in SQL
    • Creating Temporary Tables in SQL — Methods, Trade-offs, and Best Practices
  • topic: analyze (30)
    • Understanding Data Analysis
    • Data Organization in Analysis
    • Sorting and Filtering in Data Analysis
    • Sorting Data in Spreadsheets
    • Sorting and Filtering Data in SQL Using ORDER BY and WHERE
    • Data Formatting and Unit Conversion in Spreadsheets
    • Data Validation in Spreadsheets
    • Combining Data Validation and Conditional Formatting in Spreadsheets
    • Using CONCAT in SQL to Combine Text from Multiple Columns
    • Working with Strings in Spreadsheets (LEN, LEFT, RIGHT, FIND)
    • Problem-Solving and Seeking Help in Data Analysis
    • How to Effectively Search for Solutions Online as a Data Analyst
    • Choosing the Right Tool in Data Analysis
    • Preparing Data for VLOOKUP in Spreadsheets
    • Using VLOOKUP to Combine Data Across Spreadsheets
    • Troubleshooting VLOOKUP and Building a Problem-Solving Framework
    • Using JOIN in SQL to Combine Tables
    • Subqueries in SQL
    • Aggregating Data with Subqueries, HAVING, and CASE in SQL
    • Using Spreadsheet Formulas for Sales Trend Analysis
    • Using COUNTIF and SUMIF for Conditional Aggregation in Spreadsheets
    • Using SUMPRODUCT for Advanced Spreadsheet Calculations
    • Using Pivot Tables for Calculations and Trend Analysis
    • Using Pivot Table Filters and Calculated Fields for Deeper Analysis
    • Comparing Calculations in Spreadsheets and SQL
    • Embedding Calculations in SQL Queries
    • Using GROUP BY and ORDER BY for Aggregated Calculations in SQL
    • Data Validation as an Ongoing Analytical Process
    • Temporary Tables and the WITH Clause in SQL
    • Creating Temporary Tables in SQL — Methods, Trade-offs, and Best Practices
  • topic: apply (6)
    • Tailoring Your Resume
    • Using AI to Improve and Tailor Your Resume
    • Building a Professional Online Presence (Personal Brand)
    • Choosing the Right Job Platforms
    • Job Application Tracking (Using AI + Spreadsheets)
    • Networking for Job Search
  • topic: basics (10)
    • Introduction to Python and Programming Fundamentals
    • Python Fundamentals
    • Jupyter Notebook and Coding Environments
    • Object-Oriented Programming (OOP) in Python
    • Variables in Python
    • Naming Conventions and Restrictions in Python
    • Data Types and Type Conversion in Python
    • Functions in Python
    • Code Reusability, Modularity, and Clean Code in Python
    • Comments, Algorithms, and Docstrings in Python
  • topic: bias_ethics (8)
    • Understanding Bias in Data Analysis
    • Sampling Bias and Unbiased Data
    • Common Types of Data Bias
    • Identifying Good Data Sources (ROCCC Framework)
    • Identifying Bad Data Sources (When Data Does Not ROCCC)
    • Data Ethics in Data Analysis
    • Data Privacy in Data Ethics
    • Open Data and Openness in Data Ethics
  • topic: calc (8)
    • Using Spreadsheet Formulas for Sales Trend Analysis
    • Using COUNTIF and SUMIF for Conditional Aggregation in Spreadsheets
    • Using SUMPRODUCT for Advanced Spreadsheet Calculations
    • Using Pivot Tables for Calculations and Trend Analysis
    • Using Pivot Table Filters and Calculated Fields for Deeper Analysis
    • Comparing Calculations in Spreadsheets and SQL
    • Embedding Calculations in SQL Queries
    • Using GROUP BY and ORDER BY for Aggregated Calculations in SQL
  • topic: cleaning (32)
    • The Importance of Clean Data
    • Data Integrity and Its Risks in Data Analysis
    • Aligning Data with Business Objectives
    • Handling Insufficient Data in Data Analysis
    • Population, Sample Size, and Random Sampling
    • Statistical Power in Data Analysis
    • Sample Size and Data Integrity
    • Margin of Error
    • Dirty Data vs. Clean Data
    • The Importance of Clean Data (revisited)
    • Common Issues in Dirty Data
    • Data Cleaning with Spreadsheets
    • Cleaning and Merging Multiple Datasets
    • Spreadsheet Tools for Data Cleaning
    • Using Spreadsheet Functions for Data Cleaning
    • Viewing Data Differently for More Effective Data Cleaning
    • Data Mapping and the Big Picture of Clean Data
    • Introduction to SQL
    • Spreadsheets vs. SQL
    • Core SQL Queries for Data Cleaning and Analysis
    • Cleaning Data with SQL: Removing Duplicates and Cleaning String Variables
    • Using CAST to Clean and Format Data in SQL
    • Advanced SQL Functions for Data Cleaning
    • COALESCE
    • Verifying and Reporting Data Integrity
    • Verifying Data-Cleaning Efforts
    • Verification Techniques: Using Spreadsheets and SQL to Catch Repeated Errors
    • Documenting Data-Cleaning Changes
    • Reporting Data-Cleaning Results
    • Using Feedback from Data Cleaning to Improve Data Quality
    • Refining a Resume for Data Analytics Roles
    • Exploring Data Analyst Job Opportunities
  • topic: combine (9)
    • Problem-Solving and Seeking Help in Data Analysis
    • How to Effectively Search for Solutions Online as a Data Analyst
    • Choosing the Right Tool in Data Analysis
    • Preparing Data for VLOOKUP in Spreadsheets
    • Using VLOOKUP to Combine Data Across Spreadsheets
    • Troubleshooting VLOOKUP and Building a Problem-Solving Framework
    • Using JOIN in SQL to Combine Tables
    • Subqueries in SQL
    • Aggregating Data with Subqueries, HAVING, and CASE in SQL
  • topic: control (5)
    • Boolean Data, Comparators, and Logical Operators in Python
    • Branching and Conditional Statements in Python
    • While Loops and Iteration in Python
    • For Loops in Python
    • range() Function and Loop Control in Python
  • topic: data analysis (202)
    • The three steps of Bayesian data analysis
    • General Notation for Statistical Inference
    • Bayesian Inference
    • Discrete Bayesian Examples – Genetics and Spell Checking (with θ)
    • Probability as a Measure of Uncertainty
    • Example — Probabilities from Football Point Spreads
    • Example — Calibration for Record Linkage
    • Some Useful Results from Probability Theory
    • Computation and Software
    • Bayesian Inference in Applied Statistics
    • Estimating a Probability from Binomial Data
    • Posterior as a Compromise Between Data and Prior Information
    • Summarizing Posterior Inference
    • Informative Prior Distributions
    • Normal Distribution with Known Variance
    • Other Standard Single-Parameter Models
    • Informative Prior Distribution for Cancer Rates
    • Noninformative Prior Distributions
    • Weakly Informative Prior Distributions
    • Averaging Over Nuisance Parameters
    • Normal Data with a Noninformative Prior Distribution
    • Normal Data with a Conjugate Prior Distribution
    • Multinomial Model for Categorical Data
    • Multivariate Normal Model with Known Variance
    • Multivariate Normal with Unknown Mean and Variance
    • Example: Bayesian analysis of a bioassay experiment (logistic, nonconjugate)
    • Summary of Elementary Modeling and Computation
    • Normal Approximations to the Posterior Distribution
    • Large-Sample Theory
    • Counterexamples to large-sample (asymptotic) Bayesian theorems
    • Frequency Evaluations of Bayesian Inferences
    • Bayesian interpretations of other statistical methods
    • Constructing a Parameterized Prior Distribution
    • Exchangeability and hierarchical models
    • Bayesian analysis of conjugate hierarchical models
    • Normal model with exchangeable parameters
    • Example: parallel experiments in eight schools
    • Hierarchical modeling applied to a meta-analysis
    • Weakly Informative Priors for Variance Parameters
    • The Place of Model Checking in Applied Bayesian Statistics
    • Do the Inferences from the Model Make Sense?
    • Posterior predictive checking
    • Graphical posterior predictive checks
    • Model checking for the educational testing example
    • Measures of predictive accuracy
    • Model comparison based on predictive performance
    • Model comparison using Bayes factors
    • Continuous model expansion
    • Implicit assumptions and model expansion: an example
    • Bayesian inference requires a model for data collection
    • Data-collection models and ignorability
    • Sample surveys
    • Designed experiments
    • Sensitivity and the role of randomization
    • Observational studies
    • Censoring and truncation
    • Bayesian decision theory in different contexts
    • Using regression predictions: survey incentives
    • Multistage decision making: medical screening
    • Hierarchical decision analysis for home radon
    • Personal vs. institutional decision analysis
    • Numerical integration
    • Distributional approximations
    • Direct simulation and rejection sampling
    • Importance sampling
    • How many simulation draws are needed?
    • Computing environments
    • Debugging Bayesian computing
    • Gibbs sampler
    • Metropolis and Metropolis-Hastings algorithms
    • Using Gibbs and Metropolis as building blocks
    • Inference and assessing convergence
    • Effective number of simulation draws
    • Example: hierarchical normal model
    • Efficient Gibbs samplers
    • Efficient Metropolis jumping rules
    • Further extensions to Gibbs and Metropolis
    • Hamiltonian Monte Carlo
    • Hamiltonian Monte Carlo for a hierarchical model
    • Stan: developing a computing environment
    • Finding posterior modes
    • Boundary-avoiding priors for modal summaries
    • Normal and related mixture approximations
    • Finding marginal posterior modes using EM
    • Conditional and marginal posterior approximations
    • Example: hierarchical normal model (continued)
    • Variational inference
    • Expectation propagation
    • Other approximations
    • Unknown normalizing factors
    • Conditional modeling
    • Bayesian analysis of classical regression
    • Regression for causal inference: incumbency and voting
    • Goals of regression analysis
    • Assembling the matrix of explanatory variables
    • Regularization and dimension reduction
    • Unequal variances and correlations
    • Including numerical prior information
    • Regression coefficients exchangeable in batches
    • Example: forecasting U.S. presidential elections
    • Interpreting a normal prior distribution as extra data
    • Varying intercepts and slopes
    • Computation: batching and transformation
    • Analysis of variance and the batching of coefficients
    • Hierarchical models for batches of variance components
    • Standard generalized linear model likelihoods
    • Working with generalized linear models
    • Weakly informative priors for logistic regression
    • Overdispersed Poisson regression for police stops
    • State-level opinons from national polls
    • Models for multivariate and multinomial responses
    • Loglinear models for multivariate discrete data
    • Aspects of robustness
    • Overdispersed versions of standard models
    • Posterior inference and computation
    • Robust inference for the eight schools
    • Robust regression using t-distributed errors
    • Notation
    • Multiple imputation
    • Missing data in the multivariate normal and t models
    • Example: multiple imputation for a series of polls
    • Missing values with counted data
    • Example: an opinion poll in Slovenia
    • Example: serial dilution assay
    • Example: population toxicokinetics
    • Splines and weighted sums of basis functions
    • Basis selection and shrinkage of coefficients
    • Non-normal models and regression surfaces
    • Gaussian process regression
    • Example: birthdays and birthdates
    • Latent Gaussian process models
    • Functional data analysis
    • Density estimation and regression
    • Setting up and interpreting mixture models
    • Example: reaction times and schizophrenia
    • Label switching and posterior computation
    • Unspecified number of mixture components
    • Mixture models for classification and regression
    • Bayesian histograms
    • Dirichlet process prior distributions
    • Dirichlet process mixtures
    • Beyond density estimation
    • Hierarchical dependence
    • Density regression
    • Bayesian Data Analysis
    • Why Do We Analyze Data?
    • The Process of Data Analysis
    • CRISP-DM for Data Science
    • Big Data: Definition, Characteristics, Evolution, and Business Impact
    • The First Step in Knowing Your Data
    • IEEE 754 Floating-Point Standard
    • Discovering Associations Through Data: From Everyday Patterns to Chicago Taxi Trips (September 2022)
    • Taxi Trips – 2022 dataset from the City of Chicago open data portal
    • Objective Selection of the Bin Width for a Time Histogram
    • Measuring Associations in Data
    • Measuring Associations Between Two Continuous Variables
    • Correlation Coefficients in Python (Pearson, Spearman, Kendall)
    • Karl Pearson
    • Harald Cramér
    • What Are Statistical Tests?
    • Eta Squared (η²): Effect Size in ANOVA
    • Understanding Market Baskets and Ideal Customers
    • What Can Association Rules Tell Us?
    • How Association Rules Are Discovered: Concepts, Scale, Measures, and the Apriori Approach
    • Apriori: Frequent Itemsets via the Apriori Algorithm
    • association_rules: Generating Association Rules from Frequent Itemsets (mlxtend)
    • Cross-Selling
    • Stratified Random Sampling
    • Linear Congruential Random Number Generator (LCG)
    • Partitioning Observations to Train Objective Models
    • Putting Similar Observations into Clusters
    • Clustering
    • Recency, Frequency, and Monetary Value (RFM)
    • RFM Analysis
    • Creating Segments of Observations for Business Reasons (RFM)
    • Least Squares Regression
    • Multiple Linear Regression
    • Feature Importance in Linear Regression
    • Forward Selection: Definition and Core Idea
    • Forward Selection and Model Interpretation in Linear Regression
    • Understanding Forward and Backward Stepwise Regression
    • How Shapley Values Work
    • Logistic Regression: Modeling Binary Outcomes via Odds and Log-Odds
    • Maximum Likelihood (MLE): Fitting a Distribution to Observed Data
    • Assessing Model Fit in Logistic Regression
    • Complete and Quasi-Complete Separation in Logistic Regression
    • Forward Selection with Nested Models and Deviance Tests
    • Interpreting and Assessing a Forward-Selection Logistic Regression Model for College Student Retention
    • Motivation of Decision Trees: An Incremental Model of Decision-Making
    • The CART Algorithm
    • Decision Trees as Piecewise Models and Their Predictive Structure
    • How CART Decision Trees Model Interactions
    • Cluster Profiling Using Decision Trees
    • Using Decision Trees to Explain Clustering Results
    • Assessing the Quality of Prediction Models
    • Binary Classification Models – Conceptual Framework and Evaluation Metrics
    • Nominal Classification Models: Model State and Evaluation Metrics
    • Binary Classification Model Evaluation and Threshold Optimization
    • Identifying Outliers Using Residuals and Studentized Residuals
    • AUC–ROC Curve: Evaluating Classification Model Performance
    • Lift Analysis for Direct Mail Campaigns: Concept, Process, and Business Value
    • Data Preparation & Analysis
  • topic: data analytics (225)
    • Why Data Analytics Matters Today
    • How Data Analytics Improves the Workplace
    • Data-Driven Decision-Making
    • Detectives and Data Analysts
    • The Six Phases of the Data Analysis Process
    • The Origins of Data Analysis and the Many Ways to Structure It
    • Understanding the Data Ecosystem
    • Understanding the Data Analysis Process and the Data Life Cycle
    • Understanding the Data Life Cycle
    • A Review of the Six Stages of the Data Life Cycle
    • The Stages of the Data Analysis Process and Their Roles
    • Practical Application of the Data Analysis Process
    • Analytical Skills and Their Core Components
    • Applying Analytical Skills in a Business Context
    • Analytical Thinking and Its Core Components
    • Analytical Thinking and Questions for Problem Solving
    • Root Cause Analysis and Business Applications of the Five Whys
    • Data-Driven Decision-Making and the Role of Analytical Skills
    • Case Studies in Data Analysis and the Practical Impact of Data-Driven Decision-Making
    • Overview of Core Tools Used by Data Analysts
    • The Role of Spreadsheets in Data Analysis and Basic Concepts
    • The Concept and Basic Use of SQL (Query Language)
    • The Role and Importance of Data Visualization
    • Industries Where Data Analysts Work and How Data Is Used
    • The Role of Business Tasks in Data Analysis
    • Fairness in Data Analysis
    • Key Factors to Consider When Choosing a Data Analytics Role
    • 🌱 Foundations
    • Using Data Analysis to Choose the Right Advertising Strategy
    • Understanding Common Problem Types in Data Analytics
    • Applying Data Analytics Problem Types in Real Business Scenarios
    • Why Asking the Right Questions Matters in Data Analytics
    • The Relationship Between Data and Decision-Making
    • Quantitative and Qualitative Data in Decision-Making
    • Data Creates Value Only When It Is Communicated
    • The Difference Between Data and Metrics, and the Role of Metrics
    • Dashboards
    • Mathematical Thinking
    • Spreadsheets in Data Analysis
    • Building and Organizing a Spreadsheet
    • How Data Analysts Use Spreadsheets
    • Spreadsheet Calculations with Formulas
    • Common Spreadsheet Errors and How to Fix Them
    • Spreadsheet Functions
    • Defining the Problem Domain
    • Context and Bias in Data Analysis
    • Stakeholder Expectations in Data Analysis
    • Staying Focused on the Project Objective
    • Clear Communication with Stakeholders and Teams
    • Adapting to Communication Expectations at Work
    • Managing Stakeholder Expectations and Project Constraints
    • Balancing Speed and Accuracy in Data Analysis
    • Sharing Data to Drive Impact
    • Effective Meetings
    • Conflict Resolution in the Workplace
    • 🎯 Data-Driven Decisions
    • How Data Is Generated and Collected
    • Choosing the Right Data to Collect
    • Understanding Data Types and Data Formats
    • Structured Data and Data Models
    • Data Types in Spreadsheets
    • Data Tables (Tabular Data)
    • Wide Data vs. Long Data
    • Understanding Bias in Data Analysis
    • Sampling Bias and Unbiased Data
    • Common Types of Data Bias
    • Identifying Good Data Sources (ROCCC Framework)
    • Identifying Bad Data Sources (When Data Does Not ROCCC)
    • Data Ethics in Data Analysis
    • Data Privacy in Data Ethics
    • Open Data and Openness in Data Ethics
    • Databases and Relational Database Concepts
    • Metadata in Databases
    • Metadata Repositories and Data Governance
    • Accessing Data: Internal and External Sources
    • Importing Data into Spreadsheets
    • Sorting and Filtering Data in Spreadsheets
    • BigQuery Account Types
    • Querying Data with SQL
    • Organizing Data for Personal and Work Projects
    • Data Security in Spreadsheets
    • 📦 Data Preparation
    • The Importance of Clean Data
    • Data Integrity and Its Risks in Data Analysis
    • Aligning Data with Business Objectives
    • Handling Insufficient Data in Data Analysis
    • Population, Sample Size, and Random Sampling
    • Statistical Power in Data Analysis
    • Sample Size and Data Integrity
    • Margin of Error
    • Dirty Data vs. Clean Data
    • The Importance of Clean Data (revisited)
    • Common Issues in Dirty Data
    • Data Cleaning with Spreadsheets
    • Cleaning and Merging Multiple Datasets
    • Spreadsheet Tools for Data Cleaning
    • Using Spreadsheet Functions for Data Cleaning
    • Viewing Data Differently for More Effective Data Cleaning
    • Data Mapping and the Big Picture of Clean Data
    • Introduction to SQL
    • Spreadsheets vs. SQL
    • Core SQL Queries for Data Cleaning and Analysis
    • Cleaning Data with SQL: Removing Duplicates and Cleaning String Variables
    • Using CAST to Clean and Format Data in SQL
    • Advanced SQL Functions for Data Cleaning
    • COALESCE
    • Verifying and Reporting Data Integrity
    • Verifying Data-Cleaning Efforts
    • Verification Techniques: Using Spreadsheets and SQL to Catch Repeated Errors
    • Documenting Data-Cleaning Changes
    • Reporting Data-Cleaning Results
    • Using Feedback from Data Cleaning to Improve Data Quality
    • Refining a Resume for Data Analytics Roles
    • Exploring Data Analyst Job Opportunities
    • 🧽 Data Cleaning & Preparation
    • Understanding Data Analysis
    • Data Organization in Analysis
    • Sorting and Filtering in Data Analysis
    • Sorting Data in Spreadsheets
    • Sorting and Filtering Data in SQL Using ORDER BY and WHERE
    • Data Formatting and Unit Conversion in Spreadsheets
    • Data Validation in Spreadsheets
    • Combining Data Validation and Conditional Formatting in Spreadsheets
    • Using CONCAT in SQL to Combine Text from Multiple Columns
    • Working with Strings in Spreadsheets (LEN, LEFT, RIGHT, FIND)
    • Problem-Solving and Seeking Help in Data Analysis
    • How to Effectively Search for Solutions Online as a Data Analyst
    • Choosing the Right Tool in Data Analysis
    • Preparing Data for VLOOKUP in Spreadsheets
    • Using VLOOKUP to Combine Data Across Spreadsheets
    • Troubleshooting VLOOKUP and Building a Problem-Solving Framework
    • Using JOIN in SQL to Combine Tables
    • Subqueries in SQL
    • Aggregating Data with Subqueries, HAVING, and CASE in SQL
    • Using Spreadsheet Formulas for Sales Trend Analysis
    • Using COUNTIF and SUMIF for Conditional Aggregation in Spreadsheets
    • Using SUMPRODUCT for Advanced Spreadsheet Calculations
    • Using Pivot Tables for Calculations and Trend Analysis
    • Using Pivot Table Filters and Calculated Fields for Deeper Analysis
    • Comparing Calculations in Spreadsheets and SQL
    • Embedding Calculations in SQL Queries
    • Using GROUP BY and ORDER BY for Aggregated Calculations in SQL
    • Data Validation as an Ongoing Analytical Process
    • Temporary Tables and the WITH Clause in SQL
    • Creating Temporary Tables in SQL — Methods, Trade-offs, and Best Practices
    • 📊 Analyze Data
    • Data Visualization
    • Connecting Data and Images
    • Creating Powerful Data Visualizations: Focus, Structure, and Analytical Purpose
    • Static vs. Dynamic Data Visualizations: Design Tradeoffs, Control, and Interactivity
    • Elements of Art in Data Visualization: Line, Shape, Color, Space, and Movement
    • Choosing the Right Visualization: Audience-Centered Design and Chart Selection
    • Design Thinking in Data Visualization: A User-Centered Framework
    • Accessibility in Data Visualization: Designing for Everyone
    • Introduction to Tableau
    • Getting Started with Tableau Public
    • Creating a CO₂ Emissions Visualization in Tableau Public
    • Effective vs. Ineffective Data Visualizations in Tableau
    • Using Creativity in Tableau
    • Linking Multiple Datasets in Tableau Public
    • Data Storytelling: Giving Numbers a Clear and Convincing Voice
    • Engaging Your Audience in Data Storytelling: Identifying the Key Message
    • Data Dashboards: Organizing Insight for Real-Time Decision Making
    • Using Filters to Create Compelling and Focused Visuals
    • Structuring a Persuasive Data Presentation: Turning Insights into Story
    • Designing Effective Data Presentation Slides: Structure, Visuals, and Professional Impact
    • Using a Strategic Framework to Structure Data Presentations
    • Weaving Data into Presentations: Hypotheses, Context, and the McCandless Method
    • Presentation Skills for Data Analysts: Delivering Insights with Confidence
    • Presenting Like a Pro: Best Practices for Data Analysts
    • Preparing for Q&A: Anticipating and Responding to Stakeholder Questions
    • Handling Objections in Data Presentations: Responding with Confidence and Clarity
    • Q&A Best Practices: Answering Questions with Clarity and Confidence
    • 🎨 Data Visualization
    • Introduction to Python and Programming Fundamentals
    • Python Fundamentals
    • Jupyter Notebook and Coding Environments
    • Object-Oriented Programming (OOP) in Python
    • Variables in Python
    • Naming Conventions and Restrictions in Python
    • Data Types and Type Conversion in Python
    • Functions in Python
    • Code Reusability, Modularity, and Clean Code in Python
    • Comments, Algorithms, and Docstrings in Python
    • Boolean Data, Comparators, and Logical Operators in Python
    • Branching and Conditional Statements in Python
    • While Loops and Iteration in Python
    • For Loops in Python
    • range() Function and Loop Control in Python
    • Strings in Python
    • String Indexing and Slicing in Python
    • String Formatting with .format() in Python
    • Data Types vs Data Structures & Introduction to Lists
    • Modifying Lists in Python
    • Tuples in Python
    • Advanced Use of Loops, Lists, Tuples & List Comprehension
    • Dictionaries in Python
    • Advanced Dictionary Usage in Python
    • Sets in Python
    • Libraries, Packages, and Modules in Python
    • Introduction to NumPy and Vectorization
    • NumPy Arrays (ndarray) and Core Concepts
    • Introduction to Pandas (Data Analysis Library)
    • Pandas DataFrame & Series
    • Boolean Masking in Pandas
    • Grouping and Aggregation in Pandas (groupby, agg)
    • Combining Data in Pandas (concat and merge)
    • 🐍 Data Analysis Using Python
    • Transferable Skills
    • Career Identity Statement
    • Career Dreamer (AI Tool for Career Exploration)
    • Job Search Plan (Using AI Tools)
    • Tailoring Your Resume
    • Using AI to Improve and Tailor Your Resume
    • Building a Professional Online Presence (Personal Brand)
    • Choosing the Right Job Platforms
    • Job Application Tracking (Using AI + Spreadsheets)
    • Networking for Job Search
    • Interview Preparation
    • STAR Method (Behavioral Interview)
    • Using AI (NotebookLM) for Interview Preparation
    • Practicing Interviews with AI (Gemini Live)
    • Post-Interview Strategy
    • 💼 Job Search
    • 📈 Data Analytics
  • topic: data preparation (57)
    • Why Do We Analyze Data?
    • The Process of Data Analysis
    • CRISP-DM for Data Science
    • Big Data: Definition, Characteristics, Evolution, and Business Impact
    • The First Step in Knowing Your Data
    • IEEE 754 Floating-Point Standard
    • Discovering Associations Through Data: From Everyday Patterns to Chicago Taxi Trips (September 2022)
    • Taxi Trips – 2022 dataset from the City of Chicago open data portal
    • Objective Selection of the Bin Width for a Time Histogram
    • Measuring Associations in Data
    • Measuring Associations Between Two Continuous Variables
    • Correlation Coefficients in Python (Pearson, Spearman, Kendall)
    • Karl Pearson
    • Harald Cramér
    • What Are Statistical Tests?
    • Eta Squared (η²): Effect Size in ANOVA
    • Understanding Market Baskets and Ideal Customers
    • What Can Association Rules Tell Us?
    • How Association Rules Are Discovered: Concepts, Scale, Measures, and the Apriori Approach
    • Apriori: Frequent Itemsets via the Apriori Algorithm
    • association_rules: Generating Association Rules from Frequent Itemsets (mlxtend)
    • Cross-Selling
    • Stratified Random Sampling
    • Linear Congruential Random Number Generator (LCG)
    • Partitioning Observations to Train Objective Models
    • Putting Similar Observations into Clusters
    • Clustering
    • Recency, Frequency, and Monetary Value (RFM)
    • RFM Analysis
    • Creating Segments of Observations for Business Reasons (RFM)
    • Least Squares Regression
    • Multiple Linear Regression
    • Feature Importance in Linear Regression
    • Forward Selection: Definition and Core Idea
    • Forward Selection and Model Interpretation in Linear Regression
    • Understanding Forward and Backward Stepwise Regression
    • How Shapley Values Work
    • Logistic Regression: Modeling Binary Outcomes via Odds and Log-Odds
    • Maximum Likelihood (MLE): Fitting a Distribution to Observed Data
    • Assessing Model Fit in Logistic Regression
    • Complete and Quasi-Complete Separation in Logistic Regression
    • Forward Selection with Nested Models and Deviance Tests
    • Interpreting and Assessing a Forward-Selection Logistic Regression Model for College Student Retention
    • Motivation of Decision Trees: An Incremental Model of Decision-Making
    • The CART Algorithm
    • Decision Trees as Piecewise Models and Their Predictive Structure
    • How CART Decision Trees Model Interactions
    • Cluster Profiling Using Decision Trees
    • Using Decision Trees to Explain Clustering Results
    • Assessing the Quality of Prediction Models
    • Binary Classification Models – Conceptual Framework and Evaluation Metrics
    • Nominal Classification Models: Model State and Evaluation Metrics
    • Binary Classification Model Evaluation and Threshold Optimization
    • Identifying Outliers Using Residuals and Studentized Residuals
    • AUC–ROC Curve: Evaluating Classification Model Performance
    • Lift Analysis for Direct Mail Campaigns: Concept, Process, and Business Value
    • Data Preparation & Analysis
  • topic: ddd (27)
    • Using Data Analysis to Choose the Right Advertising Strategy
    • Understanding Common Problem Types in Data Analytics
    • Applying Data Analytics Problem Types in Real Business Scenarios
    • Why Asking the Right Questions Matters in Data Analytics
    • The Relationship Between Data and Decision-Making
    • Quantitative and Qualitative Data in Decision-Making
    • Data Creates Value Only When It Is Communicated
    • The Difference Between Data and Metrics, and the Role of Metrics
    • Dashboards
    • Mathematical Thinking
    • Spreadsheets in Data Analysis
    • Building and Organizing a Spreadsheet
    • How Data Analysts Use Spreadsheets
    • Spreadsheet Calculations with Formulas
    • Common Spreadsheet Errors and How to Fix Them
    • Spreadsheet Functions
    • Defining the Problem Domain
    • Context and Bias in Data Analysis
    • Stakeholder Expectations in Data Analysis
    • Staying Focused on the Project Objective
    • Clear Communication with Stakeholders and Teams
    • Adapting to Communication Expectations at Work
    • Managing Stakeholder Expectations and Project Constraints
    • Balancing Speed and Accuracy in Data Analysis
    • Sharing Data to Drive Impact
    • Effective Meetings
    • Conflict Resolution in the Workplace
  • topic: deep learning (18)
    • What is a Neural Network?
    • Supervised Learning and Neural Networks
    • Why Deep Learning is Taking Off
    • Geoffrey Hinton Interview
    • Binary Classification and Logistic Regression (Neural Network Basics)
    • Logistic Regression (Binary Classification Model)
    • Logistic Regression – Loss Function and Cost Function
    • Gradient Descent in Logistic Regression
    • Derivatives
    • More Derivative Examples
    • Computation Graph
    • Derivatives with a Computation Graph
    • Logistic Regression Gradient Descent
    • Gradient Descent on m Training Examples
    • Vectorization in Logistic Regression
    • More Vectorization Examples
    • Vectorizing Logistic Regression
    • Deep Learning
  • topic: dirty (9)
    • Dirty Data vs. Clean Data
    • The Importance of Clean Data (revisited)
    • Common Issues in Dirty Data
    • Data Cleaning with Spreadsheets
    • Cleaning and Merging Multiple Datasets
    • Spreadsheet Tools for Data Cleaning
    • Using Spreadsheet Functions for Data Cleaning
    • Viewing Data Differently for More Effective Data Cleaning
    • Data Mapping and the Big Picture of Clean Data
  • topic: execution (11)
    • Defining the Problem Domain
    • Context and Bias in Data Analysis
    • Stakeholder Expectations in Data Analysis
    • Staying Focused on the Project Objective
    • Clear Communication with Stakeholders and Teams
    • Adapting to Communication Expectations at Work
    • Managing Stakeholder Expectations and Project Constraints
    • Balancing Speed and Accuracy in Data Analysis
    • Sharing Data to Drive Impact
    • Effective Meetings
    • Conflict Resolution in the Workplace
  • topic: foundations (27)
    • Why Data Analytics Matters Today
    • How Data Analytics Improves the Workplace
    • Data-Driven Decision-Making
    • Detectives and Data Analysts
    • The Six Phases of the Data Analysis Process
    • The Origins of Data Analysis and the Many Ways to Structure It
    • Understanding the Data Ecosystem
    • Understanding the Data Analysis Process and the Data Life Cycle
    • Understanding the Data Life Cycle
    • A Review of the Six Stages of the Data Life Cycle
    • The Stages of the Data Analysis Process and Their Roles
    • Practical Application of the Data Analysis Process
    • Analytical Skills and Their Core Components
    • Applying Analytical Skills in a Business Context
    • Analytical Thinking and Its Core Components
    • Analytical Thinking and Questions for Problem Solving
    • Root Cause Analysis and Business Applications of the Five Whys
    • Data-Driven Decision-Making and the Role of Analytical Skills
    • Case Studies in Data Analysis and the Practical Impact of Data-Driven Decision-Making
    • Overview of Core Tools Used by Data Analysts
    • The Role of Spreadsheets in Data Analysis and Basic Concepts
    • The Concept and Basic Use of SQL (Query Language)
    • The Role and Importance of Data Visualization
    • Industries Where Data Analysts Work and How Data Is Used
    • The Role of Business Tasks in Data Analysis
    • Fairness in Data Analysis
    • Key Factors to Consider When Choosing a Data Analytics Role
  • topic: framing (7)
    • Using Data Analysis to Choose the Right Advertising Strategy
    • Understanding Common Problem Types in Data Analytics
    • Applying Data Analytics Problem Types in Real Business Scenarios
    • Why Asking the Right Questions Matters in Data Analytics
    • The Relationship Between Data and Decision-Making
    • Quantitative and Qualitative Data in Decision-Making
    • Data Creates Value Only When It Is Communicated
  • topic: identity (4)
    • Transferable Skills
    • Career Identity Statement
    • Career Dreamer (AI Tool for Career Exploration)
    • Job Search Plan (Using AI Tools)
  • topic: integrity (8)
    • The Importance of Clean Data
    • Data Integrity and Its Risks in Data Analysis
    • Aligning Data with Business Objectives
    • Handling Insufficient Data in Data Analysis
    • Population, Sample Size, and Random Sampling
    • Statistical Power in Data Analysis
    • Sample Size and Data Integrity
    • Margin of Error
  • topic: interview (5)
    • Interview Preparation
    • STAR Method (Behavioral Interview)
    • Using AI (NotebookLM) for Interview Preparation
    • Practicing Interviews with AI (Gemini Live)
    • Post-Interview Strategy
  • topic: jobsearch (15)
    • Transferable Skills
    • Career Identity Statement
    • Career Dreamer (AI Tool for Career Exploration)
    • Job Search Plan (Using AI Tools)
    • Tailoring Your Resume
    • Using AI to Improve and Tailor Your Resume
    • Building a Professional Online Presence (Personal Brand)
    • Choosing the Right Job Platforms
    • Job Application Tracking (Using AI + Spreadsheets)
    • Networking for Job Search
    • Interview Preparation
    • STAR Method (Behavioral Interview)
    • Using AI (NotebookLM) for Interview Preparation
    • Practicing Interviews with AI (Gemini Live)
    • Post-Interview Strategy
  • topic: libraries (8)
    • Libraries, Packages, and Modules in Python
    • Introduction to NumPy and Vectorization
    • NumPy Arrays (ndarray) and Core Concepts
    • Introduction to Pandas (Data Analysis Library)
    • Pandas DataFrame & Series
    • Boolean Masking in Pandas
    • Grouping and Aggregation in Pandas (groupby, agg)
    • Combining Data in Pandas (concat and merge)
  • topic: metrics (3)
    • The Difference Between Data and Metrics, and the Role of Metrics
    • Dashboards
    • Mathematical Thinking
  • topic: organize (10)
    • Understanding Data Analysis
    • Data Organization in Analysis
    • Sorting and Filtering in Data Analysis
    • Sorting Data in Spreadsheets
    • Sorting and Filtering Data in SQL Using ORDER BY and WHERE
    • Data Formatting and Unit Conversion in Spreadsheets
    • Data Validation in Spreadsheets
    • Combining Data Validation and Conditional Formatting in Spreadsheets
    • Using CONCAT in SQL to Combine Text from Multiple Columns
    • Working with Strings in Spreadsheets (LEN, LEFT, RIGHT, FIND)
  • topic: prep (25)
    • How Data Is Generated and Collected
    • Choosing the Right Data to Collect
    • Understanding Data Types and Data Formats
    • Structured Data and Data Models
    • Data Types in Spreadsheets
    • Data Tables (Tabular Data)
    • Wide Data vs. Long Data
    • Understanding Bias in Data Analysis
    • Sampling Bias and Unbiased Data
    • Common Types of Data Bias
    • Identifying Good Data Sources (ROCCC Framework)
    • Identifying Bad Data Sources (When Data Does Not ROCCC)
    • Data Ethics in Data Analysis
    • Data Privacy in Data Ethics
    • Open Data and Openness in Data Ethics
    • Databases and Relational Database Concepts
    • Metadata in Databases
    • Metadata Repositories and Data Governance
    • Accessing Data: Internal and External Sources
    • Importing Data into Spreadsheets
    • Sorting and Filtering Data in Spreadsheets
    • BigQuery Account Types
    • Querying Data with SQL
    • Organizing Data for Personal and Work Projects
    • Data Security in Spreadsheets
  • topic: present (9)
    • Structuring a Persuasive Data Presentation: Turning Insights into Story
    • Designing Effective Data Presentation Slides: Structure, Visuals, and Professional Impact
    • Using a Strategic Framework to Structure Data Presentations
    • Weaving Data into Presentations: Hypotheses, Context, and the McCandless Method
    • Presentation Skills for Data Analysts: Delivering Insights with Confidence
    • Presenting Like a Pro: Best Practices for Data Analysts
    • Preparing for Q&A: Anticipating and Responding to Stakeholder Questions
    • Handling Objections in Data Presentations: Responding with Confidence and Clarity
    • Q&A Best Practices: Answering Questions with Clarity and Confidence
  • topic: principles (8)
    • Data Visualization
    • Connecting Data and Images
    • Creating Powerful Data Visualizations: Focus, Structure, and Analytical Purpose
    • Static vs. Dynamic Data Visualizations: Design Tradeoffs, Control, and Interactivity
    • Elements of Art in Data Visualization: Line, Shape, Color, Space, and Movement
    • Choosing the Right Visualization: Audience-Centered Design and Chart Selection
    • Design Thinking in Data Visualization: A User-Centered Framework
    • Accessibility in Data Visualization: Designing for Everyone
  • topic: process (8)
    • The Six Phases of the Data Analysis Process
    • The Origins of Data Analysis and the Many Ways to Structure It
    • Understanding the Data Ecosystem
    • Understanding the Data Analysis Process and the Data Life Cycle
    • Understanding the Data Life Cycle
    • A Review of the Six Stages of the Data Life Cycle
    • The Stages of the Data Analysis Process and Their Roles
    • Practical Application of the Data Analysis Process
  • topic: python (33)
    • Introduction to Python and Programming Fundamentals
    • Python Fundamentals
    • Jupyter Notebook and Coding Environments
    • Object-Oriented Programming (OOP) in Python
    • Variables in Python
    • Naming Conventions and Restrictions in Python
    • Data Types and Type Conversion in Python
    • Functions in Python
    • Code Reusability, Modularity, and Clean Code in Python
    • Comments, Algorithms, and Docstrings in Python
    • Boolean Data, Comparators, and Logical Operators in Python
    • Branching and Conditional Statements in Python
    • While Loops and Iteration in Python
    • For Loops in Python
    • range() Function and Loop Control in Python
    • Strings in Python
    • String Indexing and Slicing in Python
    • String Formatting with .format() in Python
    • Data Types vs Data Structures & Introduction to Lists
    • Modifying Lists in Python
    • Tuples in Python
    • Advanced Use of Loops, Lists, Tuples & List Comprehension
    • Dictionaries in Python
    • Advanced Dictionary Usage in Python
    • Sets in Python
    • Libraries, Packages, and Modules in Python
    • Introduction to NumPy and Vectorization
    • NumPy Arrays (ndarray) and Core Concepts
    • Introduction to Pandas (Data Analysis Library)
    • Pandas DataFrame & Series
    • Boolean Masking in Pandas
    • Grouping and Aggregation in Pandas (groupby, agg)
    • Combining Data in Pandas (concat and merge)
  • topic: sources (4)
    • Databases and Relational Database Concepts
    • Metadata in Databases
    • Metadata Repositories and Data Governance
    • Accessing Data: Internal and External Sources
  • topic: spreadsheets (6)
    • Spreadsheets in Data Analysis
    • Building and Organizing a Spreadsheet
    • How Data Analysts Use Spreadsheets
    • Spreadsheet Calculations with Formulas
    • Common Spreadsheet Errors and How to Fix Them
    • Spreadsheet Functions
  • topic: spreadsheets_sql (6)
    • Importing Data into Spreadsheets
    • Sorting and Filtering Data in Spreadsheets
    • BigQuery Account Types
    • Querying Data with SQL
    • Organizing Data for Personal and Work Projects
    • Data Security in Spreadsheets
  • topic: sql (7)
    • Introduction to SQL
    • Spreadsheets vs. SQL
    • Core SQL Queries for Data Cleaning and Analysis
    • Cleaning Data with SQL: Removing Duplicates and Cleaning String Variables
    • Using CAST to Clean and Format Data in SQL
    • Advanced SQL Functions for Data Cleaning
    • COALESCE
  • topic: story (4)
    • Data Storytelling: Giving Numbers a Clear and Convincing Voice
    • Engaging Your Audience in Data Storytelling: Identifying the Key Message
    • Data Dashboards: Organizing Insight for Real-Time Decision Making
    • Using Filters to Create Compelling and Focused Visuals
  • topic: structures (10)
    • Strings in Python
    • String Indexing and Slicing in Python
    • String Formatting with .format() in Python
    • Data Types vs Data Structures & Introduction to Lists
    • Modifying Lists in Python
    • Tuples in Python
    • Advanced Use of Loops, Lists, Tuples & List Comprehension
    • Dictionaries in Python
    • Advanced Dictionary Usage in Python
    • Sets in Python
  • topic: tableau (6)
    • Introduction to Tableau
    • Getting Started with Tableau Public
    • Creating a CO₂ Emissions Visualization in Tableau Public
    • Effective vs. Ineffective Data Visualizations in Tableau
    • Using Creativity in Tableau
    • Linking Multiple Datasets in Tableau Public
  • topic: tagging (1)
    • Documentation Tagging Guidelines
  • topic: terminology (432)
    • Subsampling
    • Class Weighting
    • SMOTE (Synthetic Minority Over-sampling Technique)
    • Oversampling
    • Low-pass Filtering
    • NearMiss (Distance-based Undersampling)
    • Cluster-based undersampling
    • Random Undersampling
    • Signal Processing
    • Time Series
    • Micro AUROC
    • Multi-label Classification
    • Micro F1
    • Single-label Classification
    • Micro Recall
    • Micro Precision
    • One-vs-Rest (OvR) AUROC
    • Macro AUROC (Macro-Averaged AUROC)
    • Macro F1
    • Macro Recall
    • Macro Precision
    • Multiclass AUROC
    • Gini Coefficient
    • Bootstrap Confidence Intervals (CIs)
    • Probability
    • Mann–Whitney U Test (also called the Wilcoxon rank-sum test)
    • Predictive Parity (Calibration)
    • Equalized Odds (Fairness)
    • Equal Opportunity (Fairness)
    • Demographic Parity (Statistical Parity)
    • Cross-Selling
    • Upselling
    • Customer Segmentation
    • SaaS (Software as a Service)
    • Valuation Metric
    • D2C (Direct-to-Consumer)
    • LTV:CAC Ratio
    • Net LTV (sometimes called Contribution LTV)
    • Gross LTV (Customer Lifetime Value)
    • Predictive LTV (pLTV)
    • Cohort-Based LTV (Simple Version)
    • Customer Lifetime
    • Gross Margin
    • Fully Loaded CAC (Customer Acquisition Cost)
    • Organic CAC (Customer Acquisition Cost)
    • Paid CAC (Customer Acquisition Cost)
    • Channel-Specific CAC (Customer Acquisition Cost)
    • Blended CAC (Customer Acquisition Cost)
    • Lead-Gen Software
    • Thompson Sampling (TS) in Bandits (Multi-Armed Bandit Problem (MAB))
    • Bayesian Decision Theory (BDT)
    • Bayesian Time Series
    • Posterior probability of uplift
    • Gaussian Processes (GPs)
    • Bayesian Neural Networks (BNNs)
    • Variational Inference (VI)
    • MCMC (Markov Chain Monte Carlo)
    • Sequential Settings
    • Frequentist
    • Binomial Likelihood
    • Posterior belief
    • Marginal Likelihood (also called The Model Evidence or Integrated Likelihood)
    • Posterior
    • Prior Belief (or Prior Probability)
    • Parameter(s) of Interest
    • Bayes’ Theorem
    • Conversion Rate Uplift
    • Bayesian Stopping Rules
    • Optimizely
    • Online Experimentation Platforms
    • Stopping Rules
    • Treatment Effect
    • Posterior Probability
    • Bayesian Sequential Testing
    • Likelihood Ratio (LR)
    • Sequential Probability Ratio Test (SPRT)
    • Pocock Method
    • O’Brien–Fleming (OBF) Method
    • Group Sequential Testing
    • Type I Error
    • Traditional A/B Test (Fixed-Horizon A/B Test)
    • Fixed-Horizon Testing
    • True Conversion Rate
    • Standard Error (SE)
    • True Mean (Population Mean)
    • Margin of Error (MoE)
    • Critical Value
    • Sample Standard Deviation
    • Sample Mean
    • Regression Coefficient
    • Proportion
    • True Population Parameter
    • Compromise Power Analysis
    • Post Hoc Power Analysis
    • A Priori Power Analysis
    • Statistical Significance
    • Z-Score
    • Two-Proportion Z-Test
    • Beta Distribution
    • Google Experiments
    • Minimum Detectable Lift (MDL)
    • Trivial Effects
    • Sample size
    • Power (1 – β)
    • Significance Level (α)
    • Effect Size (δ)
    • Hypothesis Testing
    • Ranking Algorithms
    • Probabilistic Interleaving
    • Team Draft Interleaving (TDI)
    • Balanced Interleaving
    • Causal Impact
    • Bandit Algorithms
    • A/B/n Test
    • Multivariate Test (MVT)
    • Risk of Peeking
    • Causal Inference
    • P-Value (probability value)
    • Z-Test
    • T-Test
    • Session Length
    • Revenue per User (RPU / ARPU)
    • Churn
    • Retention
    • Statistically Significant
    • IID (Independent and Identically Distributed)
    • Temporal autocorrelation (Serial Correlation)
    • Blocked Splits (Single Holdout)
    • Sliding Window (Rolling Window) Cross-Validation
    • Expanding Window Cross-Validation
    • Data Leakage
    • Stratified Group K-Fold
    • Stratified Shuffle Split
    • Multiclass stratified CV
    • k-fold cross-validation
    • Cross-Validation (CV)
    • Re-scoring
    • Drift Detection
    • Model Distillation (Knowledge Distillation)
    • Early Stopping
    • Epochs
    • Hyperparameter
    • AI (Artificial Intelligence)
    • Machine Learning (ML)
    • Medical AI
    • KYC
    • FTEs
    • AWS SageMaker
    • Vertex AI
    • OpenAI API (ML API)
    • AWS SageMaker Endpoints
    • Cloud Inference with Big Payloads
    • Cloud Inference
    • Ensemble
    • Model Weights
    • FLOPs
    • OpEx
    • LLMs (Large Language Models)
    • Recalibration
    • Reweighting
    • Continuous Retraining
    • Monitoring Pipelines
    • Active Learning
    • Bayesian Correction
    • Recalibrate Thresholds
    • Guardrails (in ML & Data Systems)
    • Model KPIs (Key Performance Indicators)
    • Lagging Indicators
    • Leading Indicators
    • Windows (in Time-Series)
    • Autoencoder
    • Frozen Encoder
    • Embedding
    • Representation Shift
    • Classifier Two-Sample Tests (C2STs)
    • Energy Distance
    • Maximum Mean Discrepancy (MMD)
    • Cardinality in Categorical Data
    • Categorical Drift
    • Cramér’s V
    • Macro Shifts
    • Categorical Explosions
    • Cohort
    • Off-Distribution
    • Discriminatory Power
    • KS Statistic (Kolmogorov–Smirnov Statistic)
    • Model Stability
    • Feature Values
    • Four-Fifths (80%) Rule
    • SLI (Service Level Indicator)
    • ROI (Return on Investment)
    • Treatment Cost
    • Incremental Revenue
    • Incremental Recovery Rate (IRR)
    • Incremental Sales
    • Random Targeting Strategy
    • Causal ML (Causal Machine Learning)
    • Cumulative Uplift
    • Population Proportion
    • Incremental Gain
    • Total Incremental Benefit (TIB)
    • Cumulative Incremental Gain (CIG)
    • Qini Curve
    • Uplift Score
    • Uplift Models
    • Ops Health Dashboard
    • SLA Breach Rate
    • SLA (Service Level Agreement)
    • Supplier Constraints
    • Long Lead Times
    • Slow-Moving SKUs
    • SKU
    • Real-Time Inventory Tracking
    • Supplier Management
    • Demand Forecasting
    • Reorder Point (ROP) Optimization
    • Safety Stock
    • Backorder Rate
    • Lost Sales Value
    • Fill Rate
    • Stockout Rate
    • Prophet — Time Series Forecasting by Facebook (Meta)
    • LSTM — Long Short-Term Memory Networks
    • ARIMA (AutoRegressive Integrated Moving Average)
    • Return Distribution
    • Value-at-Risk (VaR)
    • Risk Forecast
    • Probabilistic Scoring
    • Full Distribution
    • Continuous Probabilistic Forecasts
    • Classification Probability
    • Quantile Forecasts
    • Point Forecasts
    • Strictly Proper Scoring Rules
    • Probability Forecasts
    • Target Variable
    • Probability Density
    • Normal Distribution
    • Probability Mass
    • Probability Distribution
    • Probabilistic Forecasts
    • Deterministic forecasts
    • Cumulative Distribution Function (CDF)
    • M-Competitions (Makridakis Competitions)
    • Forecasting Benchmarks
    • Average Absolute Error (AAE)
    • Seasonal Lag
    • Simple Baseline Methods
    • Naïve Baseline Forecast
    • Forecast Error
    • Forecasting Competitions
    • Predicting Percentiles
    • Prediction Intervals (PI)
    • Quantile Regression
    • Quantile Level
    • Time Series Forecasting
    • Log-Space
    • Relative accuracy
    • R² (R-squared)
    • Long-Tail Items
    • Self-Information of Popularity
    • Relevance in Recommender Systems
    • Genre Overlap
    • Jaccard index
    • Cosine Similarity of Item Features
    • Intra-List Diversity (ILD)
    • Dominating in Recommender Systems
    • Catalog Coverage
    • User Coverage
    • Item Coverage
    • Diminishing Utility
    • DCG (Discounted Cumulative Gain)
    • Kaggle
    • TREC (Text REtrieval Conference)
    • Adaptive ECE (Expected Calibration Error with Adaptive Binning)
    • Maximum Calibration Error (MCE)
    • ROC Curve (Receiver Operating Characteristic)
    • Murphy’s Decomposition
    • Temperature Scaling
    • Platt Scaling
    • Isotonic Regression
    • Support Vector Machines (SVMs)
    • Underconfident
    • Overconfident
    • Confidence Level
    • Risk-Based Decisions
    • Neural Networks
    • Binary Cross-Entropy (BCE)
    • Loss Functions
    • Underflow
    • Logit Space
    • Logistic Regression
    • Binary Classification
    • Classification Models
    • Log-Odds
    • Softmax Function
    • Sigmoid Function
    • Squashing Function
    • Conversion Rate (CR)
    • Cost-Per-Click (CPC) Models
    • Causal Trees
    • Uplift Random Forests
    • Uplift Curve
    • Likelihood
    • Correlation
    • Causal Effect
    • Outlier
    • Mean Squared Error (MSE)
    • Regression Models
    • One-vs-Rest (OvR)
    • Multiclass Classification
    • Partial AUC (pAUC)
    • Micro AUC
    • Macro AUC
    • Median
    • Mean
    • Sensitivity in Feature Engineering
    • Encode (in Feature Engineering)
    • Normalize (in Feature Engineering)
    • Embedding Similarity
    • Computer Vision (CV)
    • Natural Language Processing (NLP)
    • Accuracy
    • Chi-square (χ²) Test
    • Kolmogorov–Smirnov (KS) Test
    • Jensen–Shannon (JS) Divergence
    • Kullback–Leibler (KL) Divergence
    • Statistical Tests
    • Seasonality
    • Concept Drift
    • Data Drift
    • Fair Lending laws
    • Basel III
    • High-Stakes Domains
    • Deep Ensembles
    • Counterfactual Explanations
    • LIME (Local Interpretable Model-agnostic Explanations)
    • SHAP (SHapley Additive exPlanations)
    • Post-hoc Explainability
    • Decision Trees
    • Linear Models
    • Caching
    • Quantization
    • ONNX (Open Neural Network Exchange)
    • Full Annotation
    • Weak Supervision
    • TPU Clusters
    • Statistical Power
    • Drift Guardrails
    • Latency Guardrails
    • Fairness Guardrails
    • DeLong’s Test
    • Dataset Shift
    • Label Noise
    • Evaluation Set
    • Clopper–Pearson Interval
    • Wilson Score Interval
    • Per-class Precision (sometimes called class-wise precision)
    • Multiclass Precision
    • Multilabel Precision
    • Weighted Averaging
    • Harmonic Mean
    • F1-score
    • Model Score
    • Bootstrap
    • Average Precision (AP)
    • Upsampling
    • Downsampling
    • Micro Averaging
    • Macro Averaging
    • AUC (Area Under the Curve)
    • Fairness parity
    • LTV (Customer Lifetime Value)
    • CAC (Customer Acquisition Cost)
    • Bayesian Inference.
    • Sequential Testing (also called sequential analysis)
    • Confidence Intervals (CIs)
    • Power Analysis
    • Interleaving Tests
    • A/B Testing
    • Time-based splits (a.k.a. Temporal Cross-Validation, Rolling Window Validation)
    • k-fold Stratified Cross-Validation (Stratified CV)
    • Compute budgets
    • Manual review minutes
    • Inference Cost (Inference $)
    • Label Drift (a.k.a. Target Drift)
    • Covariate Drift (a.k.a. Covariate Shift)
    • KS shift (Kolmogorov–Smirnov shift)
    • PSI (Population Stability Index)
    • Selection Rate
    • SLOs (Service Level Objectives)
    • Cannibalization
    • Revenue net of treatment cost
    • Incremental Conversions
    • Uplift@k
    • AUUC (Area Under the Uplift Curve)
    • Qini Coefficient
    • Crew Overtime
    • SLA Breaches
    • Overstock %
    • Stockouts
    • Continuous Ranked Probability Score (CRPS)
    • MASE (Mean Absolute Scaled Error)
    • Pinball Loss (a.k.a. Quantile Loss)
    • WMAPE (Weighted Mean Absolute Percentage Error)
    • sMAPE (Symmetric Mean Absolute Percentage Error)
    • RMSLE (Root Mean Squared Logarithmic Error)
    • Mean Absolute Error (MAE)
    • Novelty (in Recommender Systems)
    • Diversity (in Recommender Systems)
    • Coverage
    • Hit Rate (HR)
    • NDCG (Normalized Discounted Cumulative Gain)
    • Mean Average Precision (MAP)
    • Expected Calibration Error (ECE)
    • Reliability Curves (also called Calibration Curves)
    • Log Loss (also called Logarithmic Loss or Cross-Entropy Loss)
    • Brier Score
    • Calibration quality (Model Calibration)
    • Logits
    • CTR (Click-Through Rate)
    • WAPE (Weighted Absolute Percentage Error)
    • Recall
    • Uplift
    • Mean Absolute Percentage Error (MAPE)
    • Root Mean Squared Error (RMSE)
    • ROC-AUC (Receiver Operating Characteristic – Area Under Curve, = AUROC)
    • Baseline Heuristics
    • Precision (a.k.a. Positive Predictive Value, PPV)
    • Precision–Recall AUC (PR-AUC)
    • Advanced Sorting in Spreadsheets
    • Terminology
  • topic: thinking (7)
    • Analytical Skills and Their Core Components
    • Applying Analytical Skills in a Business Context
    • Analytical Thinking and Its Core Components
    • Analytical Thinking and Questions for Problem Solving
    • Root Cause Analysis and Business Applications of the Five Whys
    • Data-Driven Decision-Making and the Role of Analytical Skills
    • Case Studies in Data Analysis and the Practical Impact of Data-Driven Decision-Making
  • topic: time series (19)
    • What Are Time Series, and How Are They Used?
    • Getting Started with R
    • A Gentle Introduction to Stationarity
    • Weak and Strong Stationarity
    • Linear Processes
    • Understanding ARMA Processes
    • Computing ACFs of Causal AR(2) Processes Using Difference Equations
    • Understanding ACFs via Difference Equations for AR(p) and ARMA(p, q)
    • Best Linear Predictor of a Stationary Process
    • Sample ACF and Sample PACF
    • Preliminary Estimation for AR Models and the Yule–Walker Equations
    • Maximum Likelihood Estimation for ARMA Models (Gaussian MLE)
    • Diagnostics After Fitting a Time Series Model
    • Order Selection for Time Series Models
    • ARIMA Models: How Nonstationary Models Are Built from Stationary Ones
    • SARIMA Models: Seasonal ARIMA
    • Beyond One-Step Ahead Predictions
    • Exponential Smoothing Models
    • Time Series
  • topic: tools (8)
    • Overview of Core Tools Used by Data Analysts
    • The Role of Spreadsheets in Data Analysis and Basic Concepts
    • The Concept and Basic Use of SQL (Query Language)
    • The Role and Importance of Data Visualization
    • Industries Where Data Analysts Work and How Data Is Used
    • The Role of Business Tasks in Data Analysis
    • Fairness in Data Analysis
    • Key Factors to Consider When Choosing a Data Analytics Role
  • topic: types (7)
    • How Data Is Generated and Collected
    • Choosing the Right Data to Collect
    • Understanding Data Types and Data Formats
    • Structured Data and Data Models
    • Data Types in Spreadsheets
    • Data Tables (Tabular Data)
    • Wide Data vs. Long Data
  • topic: verify (8)
    • Verifying and Reporting Data Integrity
    • Verifying Data-Cleaning Efforts
    • Verification Techniques: Using Spreadsheets and SQL to Catch Repeated Errors
    • Documenting Data-Cleaning Changes
    • Reporting Data-Cleaning Results
    • Using Feedback from Data Cleaning to Improve Data Quality
    • Refining a Resume for Data Analytics Roles
    • Exploring Data Analyst Job Opportunities
  • topic: viz (27)
    • Data Visualization
    • Connecting Data and Images
    • Creating Powerful Data Visualizations: Focus, Structure, and Analytical Purpose
    • Static vs. Dynamic Data Visualizations: Design Tradeoffs, Control, and Interactivity
    • Elements of Art in Data Visualization: Line, Shape, Color, Space, and Movement
    • Choosing the Right Visualization: Audience-Centered Design and Chart Selection
    • Design Thinking in Data Visualization: A User-Centered Framework
    • Accessibility in Data Visualization: Designing for Everyone
    • Introduction to Tableau
    • Getting Started with Tableau Public
    • Creating a CO₂ Emissions Visualization in Tableau Public
    • Effective vs. Ineffective Data Visualizations in Tableau
    • Using Creativity in Tableau
    • Linking Multiple Datasets in Tableau Public
    • Data Storytelling: Giving Numbers a Clear and Convincing Voice
    • Engaging Your Audience in Data Storytelling: Identifying the Key Message
    • Data Dashboards: Organizing Insight for Real-Time Decision Making
    • Using Filters to Create Compelling and Focused Visuals
    • Structuring a Persuasive Data Presentation: Turning Insights into Story
    • Designing Effective Data Presentation Slides: Structure, Visuals, and Professional Impact
    • Using a Strategic Framework to Structure Data Presentations
    • Weaving Data into Presentations: Hypotheses, Context, and the McCandless Method
    • Presentation Skills for Data Analysts: Delivering Insights with Confidence
    • Presenting Like a Pro: Best Practices for Data Analysts
    • Preparing for Q&A: Anticipating and Responding to Stakeholder Questions
    • Handling Objections in Data Presentations: Responding with Confidence and Clarity
    • Q&A Best Practices: Answering Questions with Clarity and Confidence
  • topic: why (4)
    • Why Data Analytics Matters Today
    • How Data Analytics Improves the Workplace
    • Data-Driven Decision-Making
    • Detectives and Data Analysts
  • Tags overview
  • My tags: topic: advanced

My tags: topic: advanced#

With this tag

  • Data Validation as an Ongoing Analytical Process
  • Temporary Tables and the WITH Clause in SQL
  • Creating Temporary Tables in SQL — Methods, Trade-offs, and Best Practices

previous

My tags: purpose: showcase

next

My tags: topic: analyze
Edit on GitHub
Show Source

© Copyright 2024 - 2026 scikit-plots developers (BSD-3 Clause License) 0.5.dev0+git.20260720.c8f33de 2026-07-20T16:01Z.