Databases Reference
In-Depth Information
Thought Experiments
1. How would data science differ if we had a “grand unified theory
of everything”? Take this to mean a symbolic explanation of how
the world works. This one question raises a bunch of other
questions:
• Would we even need data science if we had such a theory?
• Is it even theoretically possible to have such a theory? Do such
theories lie only in the realm of, say, physics, where we can an‐
ticipate the exact return of a comet we see once a century?
• What's the critical difference between physics and data science
that makes such a theory implausible?
• Is it just accuracy? Or more generally, how much we imagine
can
be explained? Is it because we predict human behavior,
which can be affected
by
our predictions, creating a feedback
loop?
It might be useful to think of the sciences as a continuum, where
physics is all the way on the right, and as you go left, you get
more chaotic—you're adding randomness (and salary). And
where is economics on this spectrum? Marketing? Finance?
If we could model this data science stuff like we already know
how to model physics, we'd actually
know
when people will click
on what ad, just as we know where the
Mars Rover
will land.
That said, there's general consensus that the real world isn't as
well-understood, nor do we expect it to be in the future.
2. In what sense does data science deserve the word “science” in its
name?
Never underestimate the power of creativity—people often have
a vision they can describe but no method as to how to get there.
As the data scientist, you have to turn that vision into a mathe‐
matical model within certain operational constraints. You need to
state a well-defined problem, argue for a metric, and optimize for
it as well. You also have to make sure you've actually answered the
original question.
There is
art
in data science—it's in translating human problems
into the mathematical context of data science and back.
