Understanding Bias: A Pre-requisite For Trustworthy Results

It turns out that it’s shockingly easy to do some very reasonable things with data (aggregate, slice, average, etc.), and come out with answers that have 2000% error! In this post, I want to show why that’s the case using some very simple, intuitive pictures. The resolution comes from having a nice model of the world, in a framework put forward by (among others) Judea Pearl.


Populating data frame cells with more than one value

Data frames are lists …


Predictive maintenance meets predictive analytics

Danielle Dean introduces the landscape and challenges of predictive maintenance applications in the manufacturing industry.
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