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ninja3925

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data scientist at a large tech company.

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The story is actually quite interesting. The Serbs observed that a nighthawk would routinely fly the same route but their radar couldn’t lock on it unless the missile hatch were open, which they managed to elicit.

In short, it took 2 rare events to occur for it to happen.

This is not how it works. The interviewer knows 1-2 problems and there is no time for profiling since they are rushing through their day, probably focused on their day to day work. You are the least of their concern, believe me.

Source: we am a hiring manager.

Just look at health insurance companies for a prime example: they make profit by denying claims

Despite being from Europe, I find this to be a shocking and erroneous interpretation.

Clearly, health insurances have the duty to allocate limited resources (“premiums”) across members. Denying and accepting claims is the mechanism to that end. Accepting all claims would increase premiums and reduce membership (by pricing people out). Would that an ideal state? Clearly not.

I actually think war driving progress is a strong argument. Not to use any obvious examples (e.g. internet), I would argue war has a profound effect on the psyche of nations (whether they lose or win).

A few examples: - Germany rose to power in the 1800s, with a culmination at Sedan in 1870, driven by the humiliation Napoleon inflected on 60 years earlier. In 1870, Germany was a behemoth of technology (especially chemistry) & industry - France won WW1 with such heavy losses that its people said “never again” (they called WW1 the “Great War” or the “last war”). War left an indelible mark (one wished it had left the same mark on the German).

Many such examples. Countries compete and war is a great impetus to modernize the Nation.

Thanks for posting this.

I work in data science and I haven’t seen anybody claiming that PCA, an unsupervised technique, is a good classification (supervised) technique. I think this is the framing that the author is pursuing.

That said, some candidates might mention clustering because it’s easy to understand and you can apply an action (this group is high but also good customers so they get a special treatment).

True.

Also, you are probably doing it wrong by turning a matrix to matrix multiplication into a for loop (over rows). The optimal solution results in better performance

sim = np.vstack(df.col) @ vec