Vikesh Vkkoul, an analyst with an MA in Applied Economics, has a nicely done collection of articles and more of Economics and Data Science at github. He also has a good set of Data Science Resources on his site as well. Check him out.
NABE Tech Conference on Economics in the Age of Algorithms, Experiments and AI – Presentations available
I browsed to this 2018 conference site put on by the National Association of Business Economists last October 2018. The program looked top rated with Economists in Data Science presenting over three days. Truly one of those wish I had been there moments. Next best thing is the addition under the Materials tab of many of the presentations.
i personally enjoyed the presentations by Chamberlain (What Can Crowdsourced Salaries Tell Us About the Labor Market? Chief economist at Glassdoor), Dunn (From Transactions Data to Economic Statistics: Constructing Real-time, High-frequency, Geographic Measures of Consumer Spending, Federal Reserve Board), Groshen (Preparing U.S. Workers & Employers for an Autonomous Vehicle Future, Cornel), Konny (Using BIG Data to Improve the Consumer Price Index, BLS) (wow) the most with two others that are outstanding:
Katheryn Shaw (Management in the Age of AI: An Economist’s Perspective, Stanford) and Hal Varian (Automation v Procreation, Google Chief Economist) is in my humble opinion the overall winner with a presentation on automation and work.
Check it out and enjoy.
Amazon’s Secret Weapon: Economic Data Scientists
Excellent read from CNN Business, how Economists help Amazon gets its edge. It is a premise of this blog that economics puts the science in data science and that economics is great training for the data science field, bringing so much value for businesses.
https://www.cnn.com/2019/03/13/tech/amazon-economists/index.html
Proof that Economics puts the Science in Data Science: “What I’ve seen change in the industry, starting about eight years ago, is firms got more serious about using the scientific method and removing chunks of guesswork within companies,” an Amazon economist said, with a characteristic nervous laugh in between sentences. “You’re basically trying to clean up waste.”
Originally, the company brought on a team of psychologists, other scientists and product managers, but before long, it became apparent that they weren’t well suited to achieving what Amazon was ultimately after: Better performance. Economists, by contrast, were able to analyze which interventions led to higher worker productivity.
Why not use traditional data scientists? “They kind of make the point that economists have more specific skill sets that are better suited for a lot of business problems,” a former Amazon staffer said.