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It\u2019s not news that data scientists are expected to be capable in many different areas (writing software, designing experiments, analyzing data, talking to non-technical stakeholders). One thing that has been changing, though, as the field becomes a bit older and more mature, is our ideas about what data scientists should focus on to stay relevant. Should they specialize in a particular area (if so, which one)? Should they instead stay general and work across many different areas? In either case, what are the costs and benefits?\n\nThis question has prompted a number of think pieces lately, which are sometimes advocating for specializing, and sometimes pointing out the benefits of generalists. In short, if you\u2019re trying to figure out what to actually do, you might be hearing some conflicting opinions. In this episode, we break apart the arguments both ways, and maybe (hopefully?) reach a little resolution about where to go from here.