Analyze · Week 02
Decision trees, dashboards + R foundations
Continue Lecture 2 with a football decision tree, connect business intelligence to responsible dashboard use, then begin the core R ideas that make analysis reproducible.
Aug 31–Sep 4Decision trees, dashboards, and R basics
ImportantStart Lecture 2 at slide 13
Week 2 begins with 🌳 A decision tree follows game conditions. The Lecture 2 preview and links below open directly at that slide.
Focus
- Decision-tree branches and conditional patterns
- Business intelligence, KPIs, and dashboard evidence
- R packages, objects, vectors, functions, and descriptive statistics
Prepare
- Open Lecture 2 at slide 13
- Open the NYC311 dashboard
- Open your section’s R repository in Posit Cloud
Practice
- Trace and interpret a decision-tree branch
- Use dashboard evidence to support a tentative decision
- Create and inspect R objects and descriptive summaries
Learning targets
- Trace a path through a decision tree and interpret its conditional pattern.
- Explain why a strong observed pattern is not a certain prediction.
- Connect business intelligence and KPIs to recurring organizational decisions.
- Interpret a dashboard pattern while stating an important limitation.
- Explain the roles of Posit Cloud, R packages, scripts, and the console.
- Create objects with
<-and build vectors containing common R data types. - Read a function call by identifying the function, arguments, and returned value.
- Compute and interpret measures of center and spread.
Core pattern
scores <- c(82, 91, 88, 95)
mean(scores)Read this as: create an object named scores, then pass that object to the mean() function.
Lecture slides
TipLecture slide shortcuts
Click inside the slide preview, then use:
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- F to enter fullscreen;
- E to enter or exit PDF export mode;
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