Jeannette Wing, director of the Columbia Data Science Institute, sent along this link to this featured story (their phrase) on their web site.

Is data science a discipline?Data science is a field of study: one can get a degree in data science, get a job as a data scientist, and get funded to do data science research. But is data science a discipline, or will it evolve to be one, distinct from other disciplines? Here are a few meta-questions about data science as a discipline.

- What is/are the driving deep question(s) of data science? Each scientific discipline (usually) has one or more “deep” questions that drive its research agenda: What is the origin of the universe (astrophysics)? What is the origin of life (biology)? What is computable (computer science)? Does data science inherit its deep questions from all its constituency disciplines or does it have its own unique ones?

- What is the role of the domain in the field of data science? People (including this author) (Wing, J.M., Janeia, V.P., Kloefkorn, T., & Erickson, L.C. (2018)) have argued that data science is unique in that it is not just about methods, but about the use of those methods in the context of a domain—the domain of the data being collected and analyzed; the domain for which a question to be answered comes from collecting and analyzing the data. Is the inclusion of a domain inherent in defining the field of data science? If so, is the way it is included unique to data science?

- What makes data science data science? Is there a problem unique to data science that one can convincingly argue would not be addressed or asked by any of its constituent disciplines, e.g., computer science and statistics?

I don’t understand how bullet point two is supposed to distinguish data science from the more prosaically titled field of applied statistics.

The story goes on to enumerate ten research challenges in data science. Some of them are hot AI topics like ethics and fairness, some of them are computer science topics such as computing systems for data-intensive applications, and some of them are statistics topics like causal inference.