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Data Visualization with AI

Graduate math/statistics · 16 weeks · 8 adaptive modules + capstone · R/WebR, ggplot2, Quarto, Shiny & GeoGebra

This course teaches students to build statistically defensible visual arguments with AI as a collaborator, R/WebR as the verification engine, and GeoGebra as the interactive math visualization layer for roughly one-third of the course. Students learn what they know, what is weak, and where to go next through adaptive evidence across DV01–DV16 competencies.

Recommended prerequisite: Intro to AI. Students should know how to prompt, inspect, and verify AI output before using it for statistical claims. Open the student portal →
16 weeksGraduate semester pacing in 8 portal modules.
R + AIAI drafts; R verifies; students defend.
GeoGebra strandAbout one-third of the course uses interactive math + AI labs.
AdaptiveDV01–DV16 mastery map and next-best steps.
CapstoneQuarto report, Shiny app, or GeoGebra-enhanced visual explanation.
Inline R, no install required. Modules include WebR-powered labs that run small R exercises directly in the browser. RStudio/Positron, Quarto, and Shiny remain recommended for deeper capstone work, but students can complete the guided inline checks without downloading R.
Interactive Math with AI / GeoGebra strand: Modules 2, 4, and 7 form a dedicated GeoGebra pathway — visual scale/proportion, regression geometry, and dashboard slider prototypes. That makes about 6 of the 16 weeks an interactive-math-with-AI experience inside the data visualization course.

Course modules

Module 1 · Weeks 1–2

Questions, Data & Cleaning

From statistical question framing to data dictionaries, cleaning logs, and missingness audits.

Adaptive evidence: DV01–DV02

 Module 2 · Weeks 3–4

Descriptive Statistics & Visual Encoding

Use center, spread, robust summaries, axes, scales, labels, and GeoGebra scale models to make visual structure mathematically honest.

Adaptive evidence: DV03–DV04

 Module 3 · Weeks 5–6

Distributions & Group Comparisons

Visualize shape, spread, skew, outliers, group overlap, and effect size with statistical caution.

Adaptive evidence: DV05–DV06

 Module 4 · Weeks 7–8

Association, Correlation & Regression

Move from GeoGebra scatterplot geometry to fitted models, residual diagnostics, leverage, and defensible model claims.

Adaptive evidence: DV07–DV08

 Module 5 · Weeks 9–10

Uncertainty, Simulation & Probability Visuals

Make uncertainty visible through intervals, bands, bootstrap logic, Monte Carlo simulations, and convergence visuals.

Adaptive evidence: DV09–DV10

 Module 6 · Weeks 11–12

Multivariate & Time-Series Visualization

Represent higher-dimensional data and change over time without hiding structure, uncertainty, or aggregation choices.

Adaptive evidence: DV11–DV12

 Module 7 · Weeks 13–14

Dashboards, Ethics & Misleading Graphics

Use Shiny dashboards to support statistical reasoning while auditing denominators, aggregation, bias, privacy, and inference risks.

Adaptive evidence: DV13–DV14

 Module 8 · Weeks 15–16

Reproducibility, AI Audit & Capstone Prep

Turn the project into a reproducible Quarto/Shiny artifact with prompt logs, verification appendix, peer review, and statistical defense.

Adaptive evidence: DV15–DV16

Capstone

Capstone

AI-Assisted Statistical Data Story

Build a reproducible R/Quarto report or Shiny app with verified statistics, visual evidence, AI prompt log, and mathematical defense.
Why this belongs after Intro to AI Intro to AI teaches students how to work with AI. This course teaches them how to use AI on evidence — to clean, visualize, verify, critique, and defend statistical claims before moving deeper into ML, IRT, or ed-tech analytics.