A data point is a discrete unit of information. In a general sense, any single fact is a data point. In a statistical or analytical context, a data point is usually derived from a measurement and can be represented numerically and/or graphically.
As opposed a data set
a collection of related sets of information that is composed of separate elements
Hi Robert, I need some further clarifications, taking also into consideration the answer(s) by Andy Weir and the reference(s) to fact and dimension tables he provides.
Clarification No 1 (time stamp relativity of definitions) :
Let's go back to your example with the individual human. If I understood well, a "data point" (the collection of measures that describes each particular individual human) is defined by the number of DIMENSIONS one has to consider at any given moment.
Let now consider that at moment "t" we have the two measures you indicated for the human individual X, the pair: (weight, height), which defines a data point; and then let's consider a further moment "t +delta t", when we add to our dimensional model the dimension "AGE". Is it correct to state that the data point for the human individual X (and also all the other individuals) has been modified and consist - at "t +delta t" - of (weight, height, age) ?
Clarification No 2 (dimension's hierarchy level relativity of definitions) :
In my knowledge, as opposed to simple "facts", which are raw numerical data, "measures" imply some kind of processing, at least an aggregation (e.g. sum, count etc.). Let's relate the aggregation to a dimension's hierarchy + granularity of data + hierarchy level. Is it correct to state that the same definitions you gave for "data point" and "data set" apply irrespective of the level on a hierarchy one wants to make the analysis ?