Statistics and analytics are two branches of data science that share many of their early heroes, so the occasional beer is still dedicated to lively debate about where to draw the boundary between them. Practically, however, modern training programs bearing those names emphasize completely different pursuits. While analysts specialize in exploring what's in your data, statisticians focus more on inferring what's beyond it. Disclaimer: This article is about typical graduates of training programs that teach only statistics or only analytics, and it in no way disparages those who have somehow managed to bulk up both sets of muscles. In fact, elite data scientists are expected to be full experts in analytics and statistics (as well as machine learning)… and miraculously these folks do exist, though they are rare.
In a previous article, I explained that typical training programs in statistics and analytics endow graduates with different skillsets. When you're dealing with uncertainty, analysts help you ask better questions, while statisticians provide more rigorous answers. Seems like the makings of a collaboration dream, yet somehow these professions end up at one another's throats. Let's see if we can make sense of the strange war between analytics and statistics (and suggest a peace treaty). Analytics helps you form hypotheses, while statistics lets you test them.
Be careful which skills you put on a pedestal, since the effects of unwise choices can be devastating. In addition to mismanaged teams and unnecessary hires, you'll see the real heroes quitting or re-educating themselves to fit your incentives du jour. A prime example of this phenomenon is in analytics. The top trophy hire in data science is elusive, and it's no surprise: "full-stack" data scientist means mastery of machine learning, statistics, and analytics. When teams can't get their hands on a three-in-one polymath, they set their sights on luring the most impressive prize among the single-origin specialists. Today's fashion in data science favors flashy sophistication with a dash of sci-fi, making AI and machine learning darlings of the hiring circuit.
The top trophy hire in data science is elusive, and it's no surprise: a "full-stack" data scientist has mastery of machine learning, statistics, and analytics. When teams can't get their hands on a three-in-one polymath, they set their sights on luring the most impressive prize among the single-origin specialists. Which of those skills gets the pedestal? Today's fashion in data science favors flashy sophistication with a dash of sci-fi, making AI and machine learning the darlings of the job market. Alternative challengers for the alpha spot come from statistics, thanks to a century-long reputation for rigor and mathematical superiority.
In the 19th century, doctors might have prescribed mercury for mood swings and arsenic for asthma. It might not have occurred to them to wash their hands before your surgery. They weren't trying to kill you, of course--they just didn't know any better. These early doctors had valuable data scribbled in their notebooks, but each held only one piece in a grand jigsaw puzzle. Without modern tools for sharing and analyzing information--as well as a science for making sense of that data--there wasn't much to stop superstition from influencing what could be seen through a keyhole of observable facts.