Starting out as a data scientist, I struggled to understand the value economics brings. Now that I understand that data science is far more than knowing how to code, I’ve been able to identify the value that economists such as myself can bring to data science and machine learning. This article is an effort to help economists explain the value they can bring to machine learning roles, as well as help non-economists in data science understand what an economist can bring to the table.
If you’ve ever taken an economics class, you may have heard of economics defined like this:
“the branch of knowledge concerned with the production, consumption, and transfer of wealth.”
If you haven’t, you likely associate economics with labels such as “finance”, “GDP”, and “stock markets”. The field however — and something I love about economics — is far broader than these terms lead on. Economics touches history, geography, business, politics, psychology, marketing, and dozens of other subjects. If you want to dive into the possibilities, check out the Freakonomics podcast. Analysis often includes consideration of the constraints and probabilities surrounding outcomes and effects.
The breadth may lead you to believe that economists think they know everything (and in some cases, you’d be right); that they have a solution to any problem. I’d suggest though that what they really have is a problem-solving framework. The framework includes cost-benefit analysis, cost and output optimization, impact studies, game theory analysis, and — running through each of these — econometrics. Throughout the article, I’ll refer back to a classic data science problem of predicting home prices (a variant of which is also a problem that economists can solve).
My use of the word “economist” refers broadly to individuals who have completed a graduate degree (Masters or Doctorate) in some facet of economics, or who have formally worked as economists. Many of the skills I’ll describe below aren’t sufficiently developed at the undergraduate level (based on my personal experience).
#recruiting #machine-learning #economics #data-science #careers
