Critical Thinking, Writing for Data Scientists, Translate Insights

1. 20018
Focus:
Business
Model
Innovation
Data Science
Writing
June, 2017
Strengthen Data Science Insights
with Critical Thinking and Writing
Copyright 2017

2. Data Science Writing Method
Questioning
Data
Analyzing Reflecting Structuring Writing Evaluating
Writing as Critical Thinking for the Information Age
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3. Questioning
• Ask what you know and don’t know
• Identify the purpose of the inquiry
• Determine the goal
• Apply critical thinking
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4. “There are known knowns;
there are things we know we know.
We also know there are known unknowns; that is to say
we know there are some things we do not know.
But there are also unknown unknowns -- the ones we
don't know we don't know.”
Donald Rumsfeld
U.S. Department of Defense, 2012
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5. When Critical Thinking Doesn’t Show Up:
Illogical
Unclear
Prejudice
Biased
Superficial
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6. Logical
Deep
Accurate
Fair
Clear
When Critical Thinking Does Show Up:
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7. Critical Thinking Is
Source: University of South Florida Sarasota-Manatee
Clear Understandable; meaning can be grasped
Accurate Free from errors or distortions; true
Precise Exact to the necessary level of detail
Relevant Relates to the matter at hand
Deep Explains complexities and provides insight
Broad Encompasses multiple viewpoints and is comprehensive
Logical Part of the thinking makes sense together; no contradictions
Significant Focuses on the important, not trivial
Fair Justifiable; not self-serving or one-sided

8. • Clarity
• Accuracy
• Relevance
• Logicalness
• Breadth
• Precision
• Significance
• Completeness
• Fairness
• Depth
• Purposes
• Questions
• Points of
View
• Information
• Inferences
• Concepts
• Implications
• Assumptions
• Intellectual
•Humility
•Autonomy
•Integrity
•Empathy
•Courage
•Perseverance
• Confidence in
Reason
• Fair-
mindedness
The Standards The Elements
The Intellectual
Traits
Applied
To
Learn to
Develop
Aadapted from: Foundation for Critical Thinking
Critical Thinkers Seek

9. Purpose
Goal,
objective
Points of view
Frame of reference,
perspective,
orientation
Question at issue
Problem, issue
Implications and
Consequences
Information
Data, facts,
observations,
experiences
Inference +
Interpretation
Conclusions,
solutions
Theories,
definitions, axioms,
laws, principles,
models
Concepts
Assumptions
Presupposition,
taking for granted
Source: Foundation for Critical Thinking
Critical Thinking Elements of Thoughts

10. Setting Expectations: How Do You Frame?
Mode Analytics Blog Discussion with Drew Harry,
Director of Twitch’s Data Science Team
What would be a good outcome for you?
What decision are you making?
What results are you expecting to get
back, and what would you do if you got
the reverse answer?

11. Questioning
Data
Analyzing Reflecting Structuring Writing Evaluating
Data Science Writing Method
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12. Data Analyzing
• Lead with curiosity
• Identify your motivation
• Analyze data
• Visualize data
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13. Data Science Writing Method
Questioning
Data
Storytelling Reflecting Structuring Writing Evaluating
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14. Grandma Test
Photo: Ridofranz
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15. Reflecting
• Organize your information
• Identify key points/insights
• Decide what information you will
include and won’t include
• Determine the audience
• Identify key questions
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16. Reflecting Exercise
Audience:
Who will read this report?
What do they need to know?
How might they use the information?
Who is your primary/secondary audience?
Essence:
How would you describe this to your grandma?
Sorting/Prioritizing Info:
What information is needed? What isn’t?
Key Questions:
What question is not being asked?
What’s the question behind the question?
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17. Critical Reflection Questions
What insights emerged?
How do you know this to be true? What evidence supports this?
What factors were considered?
What steps were taken/not taken? What was done/not done?
What challenges/problems arose?
What variables would affect the findings?
What assumptions were made? Is there a bias in your point of view?
Is there another way to look at it? How might someone else look at the data or
interpret it? Why might someone disagree with you?
What are you unsure or skeptical about?
What’s missing? What does it not show? What are the limitations?
What might be the next steps?
Can your results be replicated?
Reflecting Tips
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18. Questioning
Data
Analyzing Reflecting Structuring Writing Evaluating
Data Science Writing Method
Copyright 2017

19. Structuring
• Determine the best way to present data
• Outline the sections of the report
• Decide when story is relevant
• Consider the placement of information
within sections
• Don’t bury the lede
• Use headings to allow readers to scan
for information
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20. Don’t Bury the Lede
Source: commons.wikimedia.org
The Lede:
the most important info
who what where when why how
The Body:
the crucial info
The Tail:
extra info

21. Objective:
What is the main question?
Executive Summary:
State the purpose, context and findings.
Context:
More detail: What is the background situation?
Key Findings and Insights:
What relevant insights support the conclusion?
Approach and Methodology:
What research and data science methods were employed?
Conclusion:
Concisely summarize the logical flow.
Back up the answer to your key question and objective.
Recommendations and Next Steps:
What further action or inquiry should happen next?
Appendix:
What additional details may be required for full understanding?
Structuring Exercise
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22. Don’t present information in the same sequence in which you discovered the
data, unless there’s a reason for it.
Don’t present a visual first. Write the story and the context, and then show
insights and visuals.
Consider the placement of visuals, making sure they don’t break up the text.
Think about the flow of information.
Use section headings to guide the reader and different audiences.
Communicate, don’t confuse!
Structuring Tips
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23. Questioning Reflecting Structuring Writing Evaluating
Data
Analyzing
Data Science Writing Method
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24. Writing
• Write in clear, understandable language
• Keep your sentences focused
• Write in active voice
• Use parallel construction
• Build sentences one after another
• Consider your audience
• Write persuasively
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25. Now, lids up time.
When you want to reference a chart, graph, or table – write:
[insert graph here].
Reference your structure and reflection notes, and create 2 page
word document brief.
No more than 1500 words.
No smaller than 12 point font size.
When you are finished print your document.
Writing Exercise
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26. Don’t forget to rewrite.
Don’t make assumptions about what the reader knows.
Avoid long, unfocused sentences.
Omit needless words.
Write in the active voice.
Be consistent with tense.
Be mindful of verb choice.
Break up long paragraphs.
Explain your thinking on paper.
Writing Tips
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27. Questioning
Data
Analyzing Reflecting Structuring Writing Evaluating
Data Science Writing Method
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28. Evaluating
• Think like a critic
• Edit your writing
• Fact-check work
• Look for inconsistencies
• Check grammar and punctuation
• Question your work
• Finalize the design elements
• Let it go
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29. Are numbers accurate?
How best can you fact-check your work?
If someone edits your document, request tracked changes so you know if the
meaning has been changed.
Do abbreviations need explanation?
What’s your editing process?
What questions might your reader have?
Are you missing any important information?
Editing Questions
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30. Evaluating Exercise
Edit your own work with a red pen.
Review from the reader’s perspective.
Look to omit needless words.
Simplify. Be concise.
Make suggested edits.
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31. Check for grammatical errors.
Edit out words not needed.
Cut down long sentences.
Make sure paragraph breaks make sense.
Be consistent with font type and size.
Make sure hyperlinks work.
Keep section headings all the same font size.
Think about how the design or placement of information will help the reader
understand the content.
Evaluating Tips
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32. Peer Review
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33. Conclusion
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34. Data Science Writing Method
Questioning
Data
Analyzing Reflecting Structuring Writing Evaluating
Writing as Critical Thinking for the Information Age
Copyright 2017

35. Questioning
Data
Analyzing Reflecting Structuring Writing Evaluating
• What is the
objective?
• Is this the
right
question?
• What does
the data tell
us?
• Grandma
test
• Identify
key
insights
• Where
does the
info go?
• Write clearly,
concisely.
• Edit
• Review from
peers
Data Science Writing Method
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36. Questioning
Data
Analyzing Reflecting Structuring Writing Evaluating
What Are We Learning as a Company?
Data Science Writing Method
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37. Resources for Critical Thinking
And Writing
Books:
The Elements of Style, Strunk and White
On Writing Well, William Zinsser
On Writing, Stephen King
The President’s Book of Secrets, David Priess
Great Books, Great Ideas, Mortimer J. Adler
Websites:
www.CriticalThinking.org
Vonnegut’s Thesis: http://www.mayaeilam.com/2012/01/01/the-
shapes-of-stories-a-kurt-vonnegut-infographic/
Structure:https://www.dlsweb.rmit.edu.au/lsu/content/2_assessment
tasks/assess_tuts/reports_ll/report.pdf
Engineering Reasoning:
http://www.criticalthinking.org/pages/engineering-reasoning/833

38. jen@reasonstreet.co
tara@reasonstreet.co
1 Little West 12th Street
New York, NY 10014
(917) 671-8169