“Data Science” is a misnomer. Science, in general, is a set of methods for learning about the world. Specific sciences are the application of these methods to particular areas of study. Physics is a science: it is the study of physical phenomena. Psychology is a science: it is the study of the psyche (i.e., the human mind). There is no science of data.
Data is a collection of facts. Data, in general, is not the subject of study. Data about something in particular, such as physical phenomena or the human mind, provide the content of study. To call oneself a “data scientist” makes no sense. One cannot study data in general. One can only study data about something in particular.
Most people who call themselves data scientists are rarely involved in science at all. Instead, their work primarily involves mathematics, and usually the branch of mathematics called statistics. They are statisticians or mathematicians, not data scientists. A few years ago, Hal Varian of Google declared that “statistician” had become the sexy job of our data-focused age. Apparently, Varian’s invitation to hold up their heads in pride was not enough for some statisticians, so they coined a new term. When something loses its luster, what do you do? Some choose to give it a new name. Thus, statisticians become data scientists and data becomes “Big Data.” New names, in and of themselves, change nothing but perception; nothing of substance is gained. Only by learning to engage in data sensemaking well will we do good for the world. Only by doing actual good for the world will we find contentment.
So, you might be wondering why anyone should care if statisticians choose to call themselves data scientists, a nonsensical name. I care because people who strive to make sense of data should, more than most, be sensitive to the deafening noise that currently makes the knowledge that resides in data so difficult to find. The term “data scientist” is just another example of noise. It adds confusion to an overly and increasingly complicated world.
P.S. I realize that the term “data science” is only one of many misnomers that confuse the realm of data sensemaking. I myself am guilty of using another: “business intelligence.” This term is a misnomer (and an oxymoron as well) in that, as with data science, when it is practiced effectively, business intelligence is little more than another name for statistics. It has rarely been practiced effectively, however. Most of the work and products that bear the name business intelligence have delivered overwhelming mounds of data that is almost entirely noise.