ISSUE 390 ·   June 7, 2022

Insight

We Should Phase the "SQL Interview" out

Coding interviews are a contentious topic. Some people strongly dislike them and others view them as a necessity. Randy Au argues here that what we’re really looking for when testing SQL is “the ability to work with relational data” — which makes a good bit of sense to me and can be evalutated in a number of other ways.
Counting Stuff | Randy Au

Locally Optimal

Sean Taylor, who you may know as author of the popular forecasting library Prophet, launched a new newsletter that I’m excited to follow along with. In this first issue, he poses questions about the usefulness of A/B testing and whether generative models could provide a helping hand.
Sean J. Taylor

Collective data rights can stop big tech from obliterating privacy

“Individuals should not have to fight for their data privacy rights and be responsible for every consequence of their digital actions.” Instead, collective data rights are what can stop Big Tech from obliterating our privacy.
Martin Tisnearchive 

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Tutorials, Projects & Opinions

Framing Feature Engineering for Machine Learning: A Generative Model of Homeowners’ Likelihood to Sell

This write-up covers one data team’s journey to estimating a home’s likelihood of selling through generative models. I particularly enjoyed the plethora of details on their feature engineering approach, including examples of questions they posed e.g. “What makes a home more or less valuable to its owner?”
Compass True North | Foster Provost & Panos Ipeirotis

Duo, the Push, and the Bandits

Vicki Boykis digs into the not-so-friendly green owl that is Duolingo: its user experience, north star metrics, and multi-armed bandit notification system. This is a fun one, with plenty of links out to more bandit-related reading if you want to go deep.
Normcore Tech | Vicki Boykis

A Framework for Embedding Decision Intelligence into your Organization

“Decision intelligence” is a term that has been gaining traction over the last several years. More and more the problem isn’t getting the data, but rather using it effectively to make decisions. Whether for you that’s through analysis or modeling or both, this practical framework might be worth taking for a spin.
Erik Balodis

What I Learned from Apply(Meetup) 2022

Apply(Meetup) recently hosted a number of talks across topics in machine learning, data science, and engineering. Luckily, you didn’t have to be there to learn from them. James Le provides us with concise summaries for a variety of talks worth checking out.

Project Templates for Data Scientists

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Resources

Data for Society

If you’re looking for data to power your next side project or are just the curious type, Microsoft has curated a collection of open datasets across a wide variety of subjects: from broadband data usage to banknote imaging to glacier mappings and more.
Microsoft

Friendlier SQL with DuckDB

DuckDB released a post on a set of new user-friendly SQL features that I hadn’t seen before. I’m excited to try these out and see more similar functionality hit the mainstream as the language continues to evolve.
DuckDB | Alex Monahan

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