Principles of Data Visualization
A teaching page on what makes a visualization excellent: clear questions, comparisons, graphical integrity, data-ink ratio, uncertainty, and responsible design choices.
Open resourceAnalytics and Data Structures using AI
Static teaching pages and reports for the data science course: visualization principles, storytelling examples, chart critique, and survey analysis outputs. These are meant for classroom discussion, self-study, and reuse across course runs.
A teaching page on what makes a visualization excellent: clear questions, comparisons, graphical integrity, data-ink ratio, uncertainty, and responsible design choices.
Open resource
A gallery-style lesson contrasting plain data presentation with story-driven data communication, using memorable external examples and discussion prompts.
Open resource
A principle-by-principle guide to turning analysis into a memorable argument: audience, claim, stakes, analytic arc, focus, annotation, and ethical persuasion.
Open resource
A single-chart storytelling example using airplane crash records, global passenger growth, and plain-language safety milestones such as terrain alarms, crew training, and safety audits.
Open resource
A static report from the course survey analysis, comparing groups such as Wizards vs Explorers and other response segments with charts and interpretation notes.
Open resource
A mini-project lesson on distribution shape, digit tests, row-order forensics, GRIM checks, Benford's law, and image-duplication clues.
Open resourceThis page intentionally lists only static course resources. Interactive tools such as the points dashboard, survey entry app, and regression activities live elsewhere in the Flask app.