Emily - Chapter 1

[RohanAlexander]

Chapter 1

Theory

• For the 10 key elements of a convincing data story, I wonder if these should be broken down into a few different categories? I agree all of these are important, but I struggled to think about the interactions of them. One possible example:
  • Good question (ethics / questions), credible answer (measurement / collection / cleaning / EDA / modeling), compelling explanation (comm / repro / scaling)
• Data Collection: I love the Picasso dog example, but I think it could be worth discussing which things are getting measured and not just which features are getting measured since the former is often a major source of bias. I know this comes up in the “How do our worlds become data?” section, but it might merit a teaser?
• “How do our worlds become data?” – phenomenal. If everyone working with data read just this section alone, the practice of analysis would be much improved. I especially love the mix of anecdotes (fish size), real-world examples (maternal mortality), and theory blended together

[RohanAlexander]

Chapter 1

Theory

• For the 10 key elements of a convincing data story, I wonder if these should be broken down into a few different categories? I agree all of these are important, but I struggled to think about the interactions of them. One possible example:
  • Good question (ethics / questions), credible answer (measurement / collection / cleaning / EDA / modeling), compelling explanation (comm / repro / scaling)

Have added this categorization.

• Data Collection: I love the Picasso dog example, but I think it could be worth discussing which things are getting measured and not just which features are getting measured since the former is often a major source of bias. I know this comes up in the “How do our worlds become data?” section, but it might merit a teaser?

Great point. Have added that distinction (which things get measured and of those which features do we measure). And also added teaser to later in the chapter.

• “How do our worlds become data?” – phenomenal. If everyone working with data read just this section alone, the practice of analysis would be much improved. I especially love the mix of anecdotes (fish size), real-world examples (maternal mortality), and theory blended together