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Analytics Dispatch 2/12/2018

Quad-tum mechanics

Olympian mechanics
These stunning compilations of video and data visualization make it clear just how much speed U.S. Olympic figure skater Nathan Chen needs to pull off a quad jump or snowboarder Chloe Kimneeds to land back-to-back 1080 spins on the half-pipe. - New York Times

Visualizing Incomplete and Missing Data
The next time you're handed a messy dataset (probably tomorrow), consider exploring and exposing those gaps, rather than ignoring them. - FlowingData

[THREAD] How computer vision and natural-language processing systems reflect societal stereotypes
A rabbit hole worthy of your time: various types of machine learning bias as tracked by academic papers. - Arvind Narayanan

So, How Many ML Models You Have NOT Built?
“What will put us out of our job is Machine Learning Overkill. I have seen implementation of Machine Learning algorithms to very frivolous problems and worse still the companies have invested heavily into the idea. It is a ticking time bomb. The moment the companies realize that the ROI is negative, they will shun the Data Science practice altogether.” - Towards Data Science

New from Mode

“Group By” in SQL and Python: a Comparison
To Python or not to Python? The language you pick all depends on your goals.

PyMode: Typed interactions with the Mode Analytics API
A pip-able Python wrapper for Mode's API! (Say that three times fast).

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