Models of data range from simple summarization to detailed distribution characterization to regression models for prediction and classification. The model artifacts are typically used for insight to explain data or to answer questions in place of data.  The Wolfram Language has a rich repertoire of modeling capabilities that can be easily used directly with tabular data.

Summary Statistics »

Mean — find the mean of numbers, dates, etc.

StandardDeviation  ▪  Median  ▪  Quartiles  ▪  ...

Distribution Modeling »

SmoothKernelDistribution — fit a nonparametric distribution

FindDistribution — fit a parametric distribution

EstimatedDistribution  ▪  HistogramDistribution  ▪  KernelMixtureDistribution  ▪  DistributionFitTest  ▪  ...

Regression Modeling »

LinearModelFit — fit column data using values from the other columns

GeneralizedLinearModelFit  ▪  NonlinearModelFit  ▪  ...

Machine Learning »

Predict, Classify — predict a numerical or categorical value from a row

NetTrain — train a neural net on tabular data

FindClusters  ▪  ClusteringComponents  ▪  ClusterClassify

Survival Modeling »

SurvivalModelFit — create a survival model from event times ...

CoxModelFit  ▪  EventData  ▪  LogRankTest  ▪  ...