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phrases like “high and might” or “good and gracious” or compound-
word phrases like “devilish-holy,” “heart-sore,” or “hard-favoured.”
Note here that the digital humanities, through
the MONK Project
,
offered intense
XML descriptions
of the plays. Every single word is
given hooha, and there's something on the order of 150 different parts
of speech.
As Mark said, it's Shakespeare, so it stays awesome no matter what you
do. But they're also successively considering words as symbols, or as
thematic, or as parts of speech.
So then let's revisit the question Mark asked before showing us all these
visualizations: what's data? It's
all
data.
Here's one last piece of advice from Mark on how one acquires data.
Be a good investigator: a small polite voice which asks for data usually
gets it.
Goals of These Exhibits
These exhibits are meant to be graceful and artistic, but they should
also teach something or tell a story. At the same time, we don't want
to be overly didactic. The aim is to exist in between art and informa‐
tion. It's a funny place: increasingly we see a flattening effect when tools
are digitized and made available, so that statisticians can code like a
designer—we can make things that look like design, but is it truly
design—and similarly designers can make something that looks like
data or statistics, but is it really?
Data Science and Risk
Next we had a visitor from San Francisco—Ian Wong, who came to
tell us about doing data science on the topic of risk. Ian is an inference
scientist at Square, and he previously dropped out of the electrical
engineering PhD program at Stanford where he did research in ma‐
chine learning. (He picked up a couple master's degrees in statistics
and electrical engineering along the way.) Since coming to speak to
the class, he left Square and now works at Prismatic, a customized
newsfeed.
Ian started with three takeaways:
