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The field of Data Science can be defined in many ways. To its left is Machine Learning that emphasizes algorithms for learning, while to its right is Statistics that focuses on procedures for estimating parameters of models and determining statistical properties of those parameters. Both fields develop models to describe/predict reality based on one or more datasets. Statistics has a greater interest in making inferences or testing hypotheses based upon datasets. It also has a greater interest in fitting probability distributions (eg, are the residuals normally or exponentially distributed). The common thread is modeling. A model should be able to make predictions (where is the hurricane likely to make landfall, when will the next recession occur, etc.). In addition, it may be desirable for a model to enhance the understanding of the system under study and to address what-if type questions (perspective analytics), eg, how will traffic flow improve/degrade if a light-controlled intersection is replaced with a round-about.

A model may be viewed as replacement for a real system, phenonema to process. A model will map inputs into outputs with the goal being that for a given input, the model will produce output that approximates the output that the real system would produce. In addition to inputs and outputs, some models include state information. For example, the output of a heat pump will depend if it is in the heating or cooling state (internally this determines the direction of flow of the refrigurant). Further, some types of models are intended to mimic the behavior of the actual system and facilitate believable animation. Examples of such models are simulation …