Data Science and the art of "Formulation"
Today most Data Scientists focus on the art, science, and engineering of "Modelling" - how to build a model. But as AutoML is taking over, this skill is fast becoming obsolete.
In this talk, through a variety of examples, we will highlight an even more fundamental skill in Data Science: The Art of "Formulating" a specific Business problem, a Holistic Solution, or a Product feature as a Data Science problem.
Data Scientists, Data Engineers, Data Specialists, Machine Learning Engineers, Data Science Enthusiasts
schedule Submitted 3 years ago
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This talk outlines a principle of technical communication which seems simple at first but is devilishly difficult to abide by. It's a principle I try to keep in mind when creating videos aimed at making math and related fields more accessible, and it stands to benefit anyone who regularly needs to describe mathematical ideas in their work. Put simply, it's to resist the temptation to open a topic by describing a general result or definition, and instead let examples precede generality. More than that, it's about finding the type of example which guides the audience to rediscover the general results for themselves. We'll look, aptly enough, at examples of what I mean by this, why it's deceptively difficult to follow, and why this ordering matters.