Grouping (Genus) |
Tools (Species) |

Exploratory Data Analysis (EDA) |
Packaging of tools for quick insights; emphasis on graphics, usually for observational data: e.g., Box-Plots, Bubble Graphs, CART, Clustering, Density Plots, Histograms, k-NN, Outlier Detection, Scatterplots, Smoothing, Time-series plots, et al. |

Statistical DM (Data Mining) |
Rebranding of EDA; more discussion of some topics and less of others. Data Mining is used to straddle data analysis and data management. |

Statistical ML (Machine Learning) |
The data-analysis part of the ML packaging of statistics and data management tools with a machine learning engine, e.g., Topic Modeling, Support Vector Machines, Random Forests, Tree-Based and Rule-Based Regression and Classification, Genetic Algorithms, Gradient Boosting, Neural Networks, et al. |

Statistical Learning |
Evolving definition, at least partly a repackaging/rebranding of Statistical ML, e.g. Linear and Polynomial Regression, Logistic Regression and Discriminant Analysis; Cross-Validation/Bootstrapping, model selection and regularization methods (Ridge and Lasso); Nonlinear Models, Splines and Generalized Additive Models; Tree-Based Methods, Random Forests and Boosting; Support-Vector Machines. Some Unsupervised Learning Methods are discussed: Principal Components and Clustering (K-Means and Hierarchical). |

Statistical DS (Data Science) |
Evolving definition, rebranding of applied statistics; more discussion of some topics and less of others. Data Science was coined as a rebranding of applied statistics and now it is used to straddle data analysis and data management. |

Bayesian |
Techniques that assume a prior distribution for the parameters–its a long story, e.g., Naive Bayes, Hierarchical Bayes, et al. |

Predictive Analytics |
Rebranding of Predictive Modeling |

Multivariate Statistics |
Clustering, Factor Analysis, Principal Component Analysis, Structure Equation Modeling, et al. |

Spatial Statistics |
Packaging of tools for modeling spatial variability, e.g., Thin-plate Splines, Inverse Distance Weighting, Geographically Weighted Regression. |

Parametric Statistics |
All tools making parametric assumptions; e.g., Regression |

Nonparametric Statistics |
All tools not making parametric assumptions; they still make assumptions: e.g., Association Analysis, Neural Networks, Order Statistics, Rank Statistics, Quantile Regression, et al. |

Semi-parametric |
Family of models containing both parametric and non-parametric components (e.g. Cox-proportional hazard model) |

Categorical Data Analysis |
Statistics for a categorical response, e.g., Contingency Tables, Cochran-Mantel-Haenszel Methods, General Linear Models, Loglinear Models, Logit Models, Logistic Regression, et al. |

Time Series/Forecasting |
Tools modeling a time (or location) dependent response; e.g., ARIMA, Box-Jenkins, Correlograms, Spectral Decomposition [Census Bureau] |

Survival Analysis |
Statistics to perform time-to-event analysis (also called duration analysis), e.g., Cox Proportional Hazard, Kaplan-Meier, Life Tables, et al. |

Game Theory |
Statistics tools for modeling contests. |

Text Analytics |
Tools for extracting information from text. |

Cross Validation/Data Splitting |
Model validation techniques with no replacement for assessing statistical results, e.g., K-fold Cross-Validation, Sequential Validation |

Resampling Techniques With Replacement |
Model validation techniques with replacement for assessing statistical results, e.g., Permutation Tests, Jackknife, Bootstrapping, et al. |

Six Sigma |
Repacking of statistics common to manufacturing with clever organizational ideas. |

Quality/Process Control |
Statistics for processes, usually observational data, e.g., X-Bar Charts, R Charts, [Manufacturing] |

DoS (Design of Samples) |
Statistics for collecting sampling units, e.g., Simple Random, Systematic, Stratification, Clustering, Probabilities Proportional to Size, Multi-stage Designs, Small Area Estimation, Discrete Choice, Conjoint Analytics [Census Bureau; Marketing] |

DoE (Design of Experiments) |
Tools for assigning treatments to experimental units, e.g., Completely Randomized, Randomized Blocks, Factorial Designs, Repeated Measures, Split-Plot, Response Surface Models, Crossover Designs, Nested Designs, Clinical Modifications [Agriculture; Pharmaceuticals] |

DSim (Design of Simulation) |
Artificial generation of random processes to model uncertainty; Monte Carlo, MarkovChains, |

Stochastic Processes |
Models for processes (with uncertainty), e.g., Birth-Death Processes, Markov Chains, Markov Processes, Poison Processes, Renewal Processes, et al. |

Areas awaiting a formal name |
E.g., high dimensional problems (p>>n); et al. |

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