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Thought this was interesting. Across 160 teams of researchers, just about all failed to make good life outcome predictions on things like GPA, evictions, layoffs, and others. Data followed 4.5k families across 15 years, with 13k features (varied over time). Haven't looked at it directly yet, but will be turning the docs and data inside out... In the meantime, authors claim this as showing the limits of ML. Oh, and it's published in PNAS, so you know there's some big publication energy there.
https://www.pnas.org/content/117/15/8398
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Current:
Tableau, SQL Server/RDS, Alteryx for POC, Python, R, spark on my team but not across the org.
Former:
Power BI, SQL Server, Azure, homegrown python for virtually everything else.
Both of these orgs are led by former consultants and both relied heavily on the tooling most familiar to them when building out our stack — especially the former.
Agreed- Tableau/SQL/Python/Spark is the stack I would use with clients
It is absolutely the case that firms land projects due to expertise on specific platforms. I’d say this is more the norm than not when your buyer is a Sr Director or below.
In some firms, I’ve used Tableau, but for the most part, companies use Power BI in the world of fundraising analytics from my experience. Our coding language has consistently been “flavors” of SQL