Data Science Writing
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Critical Thinking, Writing for Data Scientists, Translate Insights
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Data Science Writing
- 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 Copyright 2017
- 3. Questioning • Ask what you know and don’t know • Identify the purpose of the inquiry • Determine the goal • Apply critical thinking Copyright 2017
- 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 Copyright 2017
- 5. When Critical Thinking Doesn’t Show Up: Illogical Unclear Prejudice Biased Superficial Copyright 2017
- 6. Logical Deep Accurate Fair Clear When Critical Thinking Does Show Up: Copyright 2017
- 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 Copyright 2017
- 12. Data Analyzing • Lead with curiosity • Identify your motivation • Analyze data • Visualize data Copyright 2017
- 13. Data Science Writing Method Questioning Data Storytelling Reflecting Structuring Writing Evaluating Copyright 2017
- 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 Copyright 2017
- 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? Copyright 2017
- 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 Copyright 2017
- 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 Copyright 2017
- 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 Copyright 2017
- 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 Copyright 2017
- 23. Questioning Reflecting Structuring Writing Evaluating Data Analyzing Data Science Writing Method Copyright 2017
- 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 Copyright 2017
- 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 Copyright 2017
- 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 Copyright 2017
- 27. Questioning Data Analyzing Reflecting Structuring Writing Evaluating Data Science Writing Method Copyright 2017
- 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 Copyright 2017
- 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 Copyright 2017
- 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. Copyright 2017
- 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 Copyright 2017
- 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 Copyright 2017
- 36. Questioning Data Analyzing Reflecting Structuring Writing Evaluating What Are We Learning as a Company? Data Science Writing Method Copyright 2017
- 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