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

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Lecture 2 · Continue at Slide 13: Decision TreeView slides in new tab
Lecture 3 · R Basics and Descriptive StatisticsView slides in new tab

This week’s materials

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