Data is the lifeline of any scientist, and the selection of data providers is critical in developing or evaluating any statistical inference or machine learning algorithm.
We briefly introduced the concept of a model in the Model categorization section in Chapter 1, Getting Started.
What constitutes a model? Wikipedia provides a reasonably good definition of a model as understood by scientists [2:1]:
A scientific model seeks to represent empirical objects, phenomena, and physical processes in a logical and objective way.
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Models that are rendered in software allow scientists to leverage computational power to simulate, visualize, manipulate and gain intuition about the entity, phenomenon or process being represented.
In statistics and the probabilistic theory, a model describes data that one might observe from a system to express any form of uncertainty and noise. A model allows us to infer rules, make predictions, and learn from data.