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.
•Inspiration prospectors If you think this means that all analysts are out of a job, you haven’t met the expert kind yet. Answering a specific question with data is much easier than generating inspiration about which questions are worth asking in the first place. I’ve written a whole article about what expert analysts do, but in a nutshell they’re all about taking a huge unexplored dataset and mining it for inspiration.
“Here’s the whole internet, go find something useful on it.”
You need speedy coding skills and a keen sense of what your leaders would find inspiring, along with all the strength of character of someone prospecting a new continent for minerals without knowing anything (yet) about what’s in the ground. The bigger the dataset and the less you know about the types of facts it could potentially cough up, the harder it is to roam around in it without wasting time. You’ll need unshakeable curiosity and the emotional resilience to handle finding a whole lot of nothing before you come up with something. It’s always easier said than done.
Analytics helps you form hypotheses. It improves the quality of your questions. Statistics helps you test hypotheses. It improves the quality of your answers.
-BY R. MADHUMITHA