  We present an approach to statistical data modeling and exploratory data
analysis called `LP Statistical Data Science.' It aims to generalize and unify
traditional and novel statistical measures, methods, and exploratory tools.
This article outlines fundamental concepts along with real-data examples to
illustrate how the `LP Statistical Algorithm' can systematically tackle
different varieties of data types, data patterns, and data structures under a
coherent theoretical framework. A fundamental role is played by specially
designed orthonormal basis of a random variable X for linear (Hilbert space
theory) representation of a general function of X, such as $\mbox{E}[Y \mid
X]$.
