Data science, data analytics, analytics

“Data science” is often used to define a (new) profession whose practitioners are capable in many or all the above areas; one often sees the term “data scientist” in job postings. While “statistician” typically implies familiarity with research methods and the collection of data for studies, “data scientist” implies the ability to work with large volumes of data generated not by studies, but by ongoing organizational processes. Due to the complexity of dealing with large datasets and data flows, most of the day-to-day work of a data scientist lies in data pipeline challenges – storing relevant data, getting it into appropriate form for analysis, and managing the real-time implementation of models. “Data analytics” and “analytics,” by contrast, are general terms used to describe the field and a comprehensive collection of associated methods. All these terms tend to be used for the application of analytic methods to data that large organziations generate or have available (“big data”).