Big Data, Computer Science and [Machine | Human] Learning
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Google’s brainteasers (that don’t work) and Johns Hopkins Biostatistics Data Analysis | Simply Statistics

Google’s brainteasers (that don’t work) and Johns Hopkins Biostatistics Data Analysis | Simply Statistics | Big Data, Computer Science and [Machine | Human] Learning | Scoop.it

This article is getting some attention, because Google's VP for people operations at Google has made public a few insights that the Google HR team has come to over the last several years. The most surprising might be:

They don't collect GPAs except for new candidatesTest scores are worthlessInterview scores weren't correlated with success.Brainteasers that Google is so famous for are worthlessBehavioral interviews are the most effective
Fabrício Barth's insight:

So I went about redesigning the types of problems our students had to tackle. Instead of assigning problems out of a book I redesigned the questions to have the following characteristics:

The were based on live data sets. I define a "live" data set as a data set that has not been used to answer the question of interest previously. The questions are problem forward, not solution backward. I would have an idea of what would likely work and what would likely not work. But I defined the question without thinking about what methods the students might use.The answer was open ended (and often not known to me in advance).The problems often had to do with unique scenarios not encountered frequently in statistics (e.g. you have a data census instead of just a sample).The problems involved methods application/development, coding, and writing/communication.
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In Head-Hunting, Big Data May Not Be Such a Big Deal

In Head-Hunting, Big Data May Not Be Such a Big Deal | Big Data, Computer Science and [Machine | Human] Learning | Scoop.it
Laszlo Bock, senior vice president of people operations at Google, says some data is essentially worthless in assessing job candidates: G.P.A.’s, for instance, and test scores.
Fabrício Barth's insight:

We found zero relationship. It’s a complete random mess, except for one guy who was highly predictive because he only interviewed people for a very specialized area, where he happened to be the world’s leading expert.

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