Presentation 4

HOW TO START
THINKING LIKE A DATA
SCIENTIST – BY THOMAS
C REDMAN

INTERESTING INSIGHT
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You don’t have to be a
data scientist or a
Bayesian statistician to
tease useful insights
from data.

he exercise is very much a how-to, each step
also illustrates an important concept in
analytics — from understanding variation to
visualization.
ach step also illustrates an important
concept in analytics — from understanding
variation to visualization.
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Lets try an exercise that illustrates an important concept in
analytics — from understanding variation to visualization.

Start with something that interests or bothers
you and form it up as a question
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“Meetings always
seem to start late. Is
that really true?”
EXAMPLE

Write down all the relevant definitions and
protocol for collecting the data.
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▰ For this particular example,
define when the meeting
actually begins. Is it the time
someone says, “Ok, let’s
begin.”? Or the time the real
business of the meeting starts?
Does kibitzing count?
▰

It is critical that you
trust the data.
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COLLECT
DATA

Sooner , you’ll be ready to start drawing some
pictures.
▰ Here, a plot
represents the date
and time of a
meeting versus the
number of minutes
late.
▰
8

Now return to the question and develop summary
statistics.
▰ In this case, “Over a two-week period,
10% of the meetings I attended started
on time. And on average, they started
12 minutes late.”
▰
9

Answer the “so what?” question.
▰ In this case, “If those
two weeks are typical, I
waste an hour a day.
And that costs the
company $X/year.”
▰
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Get a feel for variation.
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A few meetings start right on
time, others nearly a full 30
minutes late but the variation is
too great.

““What else does
the data reveal?”
1313

“What else does the data reveal?”
Five meetings that began exactly
on time were called by the
Vice President of Finance.
Evidently, she starts all her
meetings on time.
▰
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Are your results consistent with
others’ experiences in the
company?
Which starts later: conference
calls or face-to-face
meetings?
Are some days worse than
others?
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Return to step one, pose
the next group of
questions, and repeat
the process. Keep the
focus narrow — two or
three questions at most.

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