Data Visualization & Data Storytelling
Turn analysis results into visual evidence that is accurate, interpretable, and useful for next actions: from chart choice, scales and baselines, color and labels, to annotation, dashboard thinking, and reviewing misleading charts, then complete your own Evidence-Based Data Story.
The course storyline: turn analysis results into trustworthy visual evidence
The whole course follows one line: Question, Visual Encoding, Evidence, Annotation, Interpretation, Action. AI may help generate charts; people stay responsible for verification and the conclusion.
- 1. Question
What analytical question does this chart answer, and who is the audience?
- 2. Visual Encoding
Which chart type and scale best fit this question?
- 3. Evidence
What evidence does this chart actually show?
- 4. Annotation
How should the title, annotations, and source guide reading without overstating it?
- 5. Interpretation
What is observation, what is interpretation, and what is still unknown?
- 6. Action
What next action does this evidence support?
Your next step
DS 103 · Module 1: See Accurately: Visual Choice and Chart Reading
Curriculum
- Module 1See Accurately: Visual Choice and Chart ReadingStart with what a chart is for, then learn to match questions to chart types, use scales honestly, and apply accessible color and labels.
- Module 2Communicate Clearly: Storytelling, Annotation, and Decision SupportLearn to write evidence statements, use annotation and data-story structure, design a small dashboard, and review misleading charts.
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Practice and assessment
- Practice A: Chart Choice MatchMatch an analytical question to the right chart family, avoiding chart types that distort comparisons.Not done yet
- Practice B: Axis Integrity ReviewCheck whether an axis starts at zero and the scale is honest, and find visually exaggerated differences.Not done yet
- Practice C: Accessibility RepairRepair a chart that relies on color alone, adding direct labels and accessible text.Not done yet
- Practice D: Evidence Statement RewriteRewrite a claim that overstates the data into an evidence statement bounded by the data.Not done yet
- Practice E: Dashboard PrioritizationDesign a small information hierarchy for a set of KPIs, avoiding dashboard overload.Not done yet
- DS 103 pre-test and post-testPre- and post-tests check how your judgment changes on chart choice, scales and baselines, accessibility, claims and aggregation, and source/AI review.Not done yet
- Evidence-Based Data StoryTurn what you learned into your own Evidence-Based Data Story, stating the problem, data, method, verification and limitations.Not done yet
About this course
Outcomes
- Start from analytical question and audience; define one primary message per chart
- Match analytical intent to chart family; avoid chart types that distort comparisons
- Use scales honestly; understand zero baselines, log scales, sorting, and denominator effects
- Use restrained encodings; direct labels; color-independent meaning; accessible text alternatives
- Create comparison, trend, and distribution charts from a common dataset and critique them
- Write claims bounded by the data; separate observation, interpretation, and unknowns
- Use title, subtitle, annotations, source notes, and sequence to guide reading without overstating
- Identify truncated axes, dual-axis abuse, cherry-picking, aggregation masking, and AI chart errors
Prerequisites
- No programming background required
- DS 101 is recommended; DS 102 is helpful but not required
Who it is for
- NTPU students, faculty and administrative staff who communicate analysis results with charts
- Learners who completed DS 101 and want to learn visualization and data storytelling further
- Excel/spreadsheet users who build charts and want more rigorous judgment
Certificate
Earns a course completion certificate (L1); a capability certificate (L2) is also available through additional artifact review.
Requirements to earn it
- Complete all 2 course modules
- Pass the post-test assessment at 80% or higher
- Submit a project artifact and pass review
Course feedback
It takes 2 to 3 minutes and is used to improve the course.