Week 3 Notes

Key Concepts Learned

• [List main concepts from lecture] – Anscombe’s Quartet and the limits of summary statistics – Visualization in policy context – Connection to algorithmic bias and data ethics – ggplot2 fundamentals – Aesthetic mappings and geoms – Live demonstration – EDA workflow and principles – Understanding distributions and relationships – Critical focus: Data quality and uncertainty

• [Technical skills covered] ggplot(data = your_data) + aes(x = variable1, y = variable2) + geom_something() + additional_layers()

Coding Techniques

• [New R functions or approaches]

• [Quarto features learned]

Questions & Challenges

• [What I didn’t fully understand]

• [Areas needing more practice]

Connections to Policy

• [How this week’s content applies to real policy work] – Summary statistics can hide critical patterns – Outliers may represent important communities – Relationships aren’t always linear – Visual inspection reveals data quality issues

Reflection

• [What was most interesting]

• [How I’ll apply this knowledge]