teams <- c("Team 1", "The Data Scientists", "Stat Padders", "Data Divers", "")
set.seed(3570)
sample(teams, 1)[1] "Team 1"AI Activities
Activity 1 is presented by Superb Statisticians: Aaron, Connor, and Danielle.
Activity 2 is presented by Team 1: Sophia, Maya, Kaitlyn.
- Activity 3 is presented by team The Data Scientists: Eliza, Miranda, Lauren.
teams <- c("The Data Scientists", "Stat Padders", "Data Divers", "Plot Squad")
set.seed(3570)
sample(teams, 4)[1] "The Data Scientists" "Stat Padders" "Plot Squad"
[4] "Data Divers" Activity 4 is presented by team Stat Padders: Jared, DJ, Raj.
Activity 5 is presented by team Plot Squad: Dominic, Wyatt, Drishtant, Tyler.
Activity 6 is presented by team Data Divers: Karlo, Beth, Jack.
| No. | Title | Content |
|---|---|---|
| AI Activity 1: Data Importing and Hidden Assumptions | NYC Airbnb Open Data 2019 listings dataset | Importing data is not a neutral step. Import tools make assumptions about missing values, data types, dates, text encoding, and formatting. When assumptions are wrong, the… |
| AI Activity 2: Tidy vs Untidy Data: When Rules Are Not Enough | NYC Airbnb 2019 neighborhood summary dataset | Tidy data rules are helpful, but real data work often requires choices. There can be more than one reasonable tidy format, depending on the question you want to answer. |
| AI Activity 3: Dirty Data and Misleading Visualizations: When AI Plots Too Fast | Messy retail orders and returns dataset | A visualization can look polished and still be wrong. If data are dirty, a quick AI-generated plot may quietly mix categories, misread dates, double-count rows, or summarize… |
| AI Activity 4: Probabilistic and Statistical Simulation | Validate, interpret, and extend a simulation study | Simulation is useful only when the simulated process matches the actual question. In this activity, AI can help you learn, but your group must still define the trial… |
| AI Activity 5: Correlation, Regression, and Overinterpretation: When Association Is Not Explanation | Student habits and exam outcomes | Correlation and regression can summarize patterns, but those summaries are easy to overinterpret. AI may generate polished interpretations that sound persuasive while… |
| AI Activity 6: Classification, Trust, and Human Judgment | Subscription churn classification dataset | A classification model can look impressive and still be untrustworthy. AI can help write code quickly, but AI is not good at deciding which variables should be used, which… |
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