mendicantB

Hope it was helpful.

It's unclear what you t-tested here. Ideally, you would test for difference between groups of "Is there a difference in number of endorsements between people who got a "yes" in advancing to the next round or not". As a followup, is there a difference between those who's preferred was most endorsed or not?

I'm a bit stunned that you didn't recognize Language as programming language...... :(

As an example, people probably get endorsed for SQL or CSS far more than their programming language of choice that is tested in an interview.

Your analysis is extremely flawed.

I really appreciate your sharing the data with us and I like your service. But, this is a poorly done and a far from subtle plug of your business at the cost of LinkedIn.

1) Technical ability vs # of endorsements

Jesus. Hiding stats that you don't like through aggregations? And please read up on Simspons Paradox, which is clearly the case here just by looking at your plot. Try a basic t-test, or rather some statistical rigor, the next time you try to make conclusions from data.

2) Most endorsed vs Language of Choice

As pointed out, this is not the way to frame your problem. By obfuscating what's happening in your histogram (which isn't technically constructued right either) you are again hiding what you dont like through aggregation. By the way, language matters greatly here, and you'd have benefitted by standardization.

3) Your conclusion

"After running some significance testing, though" and not posting your results or methodology, which is at best questionable after reading your analysis.

Again, I enjoy your service, but blog posts on technical ability that are ironically lacking in technical ability don't really make me want to come back.

PS: A little birdie told me that endorsements are quite strong in predictive power for jobs :)

World class engineers (especially data eng) who built some amazing open source things, but internally was a mess.

The reason LinkedIn is a terrible product is because it was mired internally by chronically poor engineering leadership and product politics. Nothing ever got shipped. This new rollout is nice but 5 years too late.

I'm really hoping the MSFT buy will change things, as I really believe in the product and there are some great engineers there.

Looks awesome, are you hiring data scientists?

This is how you train soldiers. Some aren't cut out to follow.

Honestly, the paper wasn't bad, but I'm shocked they let this article get out.

Combine the author's incredibly poor understanding,shit link bait headline, and baseless article with ZERO results or novelty and this is 10 minutes of my life I want back.

Fair enough. Otherwise, does that help shed any light on this side? Thoughts?

No, you just had a bad point that you'll come to realize if you ever have to manage a team or company.

More often than not, you outsource items that are not the highest value projects your resources could be spent on. Or, you simply cannot afford to hire a specialist for a specific need. Try to find me experts for some of the pattern recognition algorithms offered who will work at your shitty startup, your shitty big-co with minimal tech wing, or your random mid-sized in the middle of nowhere. I just described 95% of companies on the planet.

And to second relix's point, try to justify implementing something that may have taken years to optimize, even if you think you can do it better. Also, do you want to maintain it? What happens if/when the company loses you? Would they rather have some poorly documented, poorly optimized in house version with nobody around who knows how it works or have a contract with another party they can count on for maintenance and support?

Is this always the case? No. Sometimes it's a core function you want to develop in house. But, it's usually considered.

As a developer, a big step in growth is realizing how to best spend your time and resources, and realizing that contracting implementation of X to further speed up building Y is a choice you'll often have to make.

That's another good point. The required computing power that is now cheap and widely available has changed our ability to even try these methods.