Where venture capital and data intersect. Every week.
Kudos for the excellent article.
Below is a good read about the design pattern for the database layer:
https://medium.com/towards-polyglot-architecture/design-patterns-for-the-database-layer-7b741b126036
Thanks for sharing insights :)
Right time in the market and right place has a lot to do with it. Like TikTok during COVID. A bit of a randomness trap - we cannot quantify / make everything a data based. Also, is this looking at male founders only? A tad confusing
great insight. interesting to read the insights about other factors, product, market, traction
very interesting article. Didn't that FAANG would have so little impact.
Again, this is fallacious.
Prediction does not imply causation.
And you can’t just condition on an outcome (startup success), because that induces collider bias.
P(tech degree | startup success) =/= P(startup success | tech degree) =/= P(startup success | do(tech degree))
Kudos for the excellent article.
Below is a good read about the design pattern for the database layer:
https://medium.com/towards-polyglot-architecture/design-patterns-for-the-database-layer-7b741b126036
Thanks for sharing insights :)
Right time in the market and right place has a lot to do with it. Like TikTok during COVID. A bit of a randomness trap - we cannot quantify / make everything a data based. Also, is this looking at male founders only? A tad confusing
great insight. interesting to read the insights about other factors, product, market, traction
very interesting article. Didn't that FAANG would have so little impact.
Again, this is fallacious.
Prediction does not imply causation.
And you can’t just condition on an outcome (startup success), because that induces collider bias.
P(tech degree | startup success) =/= P(startup success | tech degree) =/= P(startup success | do(tech degree))