Blog posts like “Reservoir Sampling and Neural Networks for Streaming Data and the IoT” or “Up-Lift Modelling for Cancer Treatments” are interesting and useful to data scientists in particular fields or to particularly creative and savvy readers. However, starting out with esoteric use cases or technical descriptions of specific technologies severely limits the “what,” “why,” and “how” of analysis, if not leaving it out altogether. Leading with current results and building value first is a boon to everyone.
I started to think about the consequences of medical misdiagnoses. What happens if someone has an infection and is told they do not by their doctor? What if someone does not have an infection but is treated as if they do? Which mistake would be worse?
If you search for top skills that data scientists need, you will find communication in the top five or ten in every post, right up there with technical skills...
"My process is very similar to that of any other analyst, so what makes a statistician unique? Our training. Most of a statistician’s training in applied statistics revolves around model building and diagnostics. Some of the things that we care a lot about (trust me, there are a lot more!) are assumptions, bias and variance, model selection, and properly answering questions."
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