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Presenting
data science
results.
By Barton Poulson
Founder, datalab.cc
Salt Lake City, UT
19 August 2016
You’ve
done your
data fu…
-You obtained the data
-You scrubbed the data
-You explored the data
-You modeled the data
-You interpreted the data
Listen to the Duke.
It don’t
mean a
thing…
if it ain’t
communicted
effectively
Address
the client’s
needs.
– Every coach everywhere
“Play the ball,
not the player.”
-Why did they hire you?
-What is their big picture?
-Did they have a specific
question for you?
-Answer their questions
directly and concisely
-Connect your answers to
their big picture
careerbuilder.com
Give
actionable
advice.
What’s a thneed?
-Address the “so what?”
-Give the next steps
-Be specific
-Operational definitions
-Recommendations must
add value
-Must have good ROI
-Remember 80/20
Data
supports
the story.
– Pablo Picasso
Computers are useless.
They can only
give you answers.
-Data doesn’t tell stories
-Interpretation is inescapably,
unavoidably by/for humans
-Data only informs and
constrains the...
-All recommendations
must be based in data
-But the connections must
be logical and believable
-Remember empathy [link]
Fantasy that fails.
Fantasy that rocks.
Reality that fails.
Beware
hubris.
A little premature.
-Don’t act more certain
than the data warrant
-Quantify uncertainty
-Do sensitivity analyses
Be
minimally
sufficient.
– Albert Einstein
Everything should be made
as simple as possible,
but not simpler.
– Ludwig Mies van der Rohe,
after Robert Browning
Less is more.
-More charts, less text
-Simplify charts
-Avoid tables
-Less text (again)
A few examples from
the spectacular people at
Darkhorse Analytics.
{darkhorseanalytics.com}
Data looks
better
naked
Salvaging
the
pie
Radar:
More evil
than pie?
When
small is
more
Clear
off the
table
Thank you,
Darkhorse
Analytics!
Tufte on
decorating
vs. lying
The goal
is
clarity…
in
analysis,
in
expression,
in
action.
And so…
Back to you, Duke.
Take
that
report…
and
make it
swing.
Your clients
will sing
your praises.
datalab.cc/blog
Presenting data science results
Presenting data science results
Presenting data science results
Presenting data science results
Presenting data science results
Presenting data science results
Presenting data science results
Presenting data science results
Presenting data science results
Presenting data science results
Presenting data science results
Presenting data science results
Presenting data science results
Presenting data science results
Presenting data science results
Presenting data science results
Presenting data science results
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Presenting data science results

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Your work needs to be communicated effectively to have impact. Learn the important elements of communicating the results of your data science analyses.

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Presenting data science results

  1. 1. Presenting data science results.
  2. 2. By Barton Poulson Founder, datalab.cc Salt Lake City, UT 19 August 2016
  3. 3. You’ve done your data fu…
  4. 4. -You obtained the data -You scrubbed the data -You explored the data -You modeled the data -You interpreted the data
  5. 5. Listen to the Duke.
  6. 6. It don’t mean a thing…
  7. 7. if it ain’t communicted effectively
  8. 8. Address the client’s needs.
  9. 9. – Every coach everywhere “Play the ball, not the player.”
  10. 10. -Why did they hire you? -What is their big picture? -Did they have a specific question for you?
  11. 11. -Answer their questions directly and concisely -Connect your answers to their big picture
  12. 12. careerbuilder.com
  13. 13. Give actionable advice.
  14. 14. What’s a thneed?
  15. 15. -Address the “so what?” -Give the next steps -Be specific -Operational definitions
  16. 16. -Recommendations must add value -Must have good ROI -Remember 80/20
  17. 17. Data supports the story.
  18. 18. – Pablo Picasso Computers are useless. They can only give you answers.
  19. 19. -Data doesn’t tell stories -Interpretation is inescapably, unavoidably by/for humans -Data only informs and constrains the stories
  20. 20. -All recommendations must be based in data -But the connections must be logical and believable -Remember empathy [link]
  21. 21. Fantasy that fails.
  22. 22. Fantasy that rocks.
  23. 23. Reality that fails.
  24. 24. Beware hubris.
  25. 25. A little premature.
  26. 26. -Don’t act more certain than the data warrant -Quantify uncertainty -Do sensitivity analyses
  27. 27. Be minimally sufficient.
  28. 28. – Albert Einstein Everything should be made as simple as possible, but not simpler.
  29. 29. – Ludwig Mies van der Rohe, after Robert Browning Less is more.
  30. 30. -More charts, less text -Simplify charts -Avoid tables -Less text (again)
  31. 31. A few examples from the spectacular people at Darkhorse Analytics. {darkhorseanalytics.com}
  32. 32. Data looks better naked
  33. 33. Salvaging the pie
  34. 34. Radar: More evil than pie?
  35. 35. When small is more
  36. 36. Clear off the table
  37. 37. Thank you, Darkhorse Analytics!
  38. 38. Tufte on decorating vs. lying
  39. 39. The goal is clarity…
  40. 40. in analysis,
  41. 41. in expression,
  42. 42. in action.
  43. 43. And so…
  44. 44. Back to you, Duke.
  45. 45. Take that report…
  46. 46. and make it swing.
  47. 47. Your clients will sing your praises.
  48. 48. datalab.cc/blog

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