There is a popular Venn diagram that purports that data science exists at the intersection of applied statistics, programming, and domain knowledge. Companies would love nothing more than to replace their statistician, software developer, and consultant with one person. Unfortunately, life experience says that very few people can be experts in all three distinct and difficult areas. So, one might say that a data scientist is a “jack of all trades, master of none”.
But in my experience, I find that many data scientists are people who have deep expertise in one area — they are statistics grad students or computer science grad students — who try to reach beyond their discipline’s traditional boundaries to secure the coveted “data scientist” title.
In other words, I would argue that data scientists are “jack of all trades, master of one“.
Take Mike, an econometrics PhD. He has a superior understanding of applied statistics, a pretty good grasp of economic theory (companies will appreciate that he took graduate micro but maybe not care about his electives), and a decent capacity to code (in so far as he needs to program his models and do his empirical work). A lot of data scientists tend to look like Mike: they have a hierarchy of skills.
Nowadays, there are “data science” programs designed so people like Mike do not have to waste their energy pivoting away from a pure econometrics skill set.
Consider Penn’s Data Science MSE. In this program, they douse you in a bit of statistics and a bit of programming. Then they send you off to do electives. I have a feeling that people will choose electives that are somewhat related to one another because it’s natural for people to want to build expertise. But let’s say you take a varied set of electives, as the Venn diagram suggests you should — is that better? I am personally doubtful. Because when the shit hits the fan, a master of none does not have the ability to diagnose problems on a deep level. The master of one will be at least as good on non-expert issues and far superior on expert issues.
Surprise, surprise — expertise still means something.