Below are three reusable activity templates designed to convert “Data Visualization Fundamentals” tasks into executable steps. Each template includes clear instructions, checkpoints, and deliverable requirements that can be applied across multiple course activities.
Template 1 — Problem Framing and Chart Strategy Brief
Purpose
Transform an analytical question into a feasible visualization plan that aligns with audience, data, and task requirements.
Inputs
- Topic or scenario
- Candidate dataset(s) or data source description
- Target audience and use context (decision, exploration, communication)
Timebox
45–60 minutes
Procedure
1) Define the analytical question
- Specify the primary question in one sentence.
- Identify the decision or action the visualization should inform.
- Classify the task type: lookup, compare, trend, part-to-whole, distribution, relationship, or flow.
2) Specify audience and constraints
- Audience: role, prior knowledge, expected time-on-task.
- Context: viewing environment (desktop/mobile/print), interactivity constraints, accessibility needs.
3) Audit data requirements
- List required variables; mark each as categorical, ordinal, quantitative, or temporal.
- Define unit of analysis and granularity (row = ?, aggregation level = ?).
- Note data quality risks (missingness, outliers, small n, sampling bias).
4) Select candidate chart types and encodings
- Map task and data types to 2–3 chart candidates (e.g., bar for categorical compare, line for temporal trend, dot/scatter for relationship).
- Propose visual encodings (position, length, angle, color hue/value, shape, size).
- Identify necessary references: zero baseline for bar charts; appropriate time order for line charts; legends, gridlines, or reference lines.
5) Define success criteria
- Measurable reading goals (e.g., “User can identify top 3 categories within 5 seconds”).
- Constraints to respect (label readability, max categories on a chart, colorblind-safe palette).
Checkpoints
- The question is specific, measurable, and answerable with the data listed.
- Chart candidates align with data and task types; any trade-offs are noted.
- Assumptions and risks are explicitly documented.
Deliverables
- 1–page Chart Strategy Brief containing:
- Problem statement and decision/use case
- Audience and context summary
- Data schema snapshot (variables, types, granularity)
- 2–3 candidate chart types with encoding rationale
- Success criteria and known risks
Evaluation Criteria
- Alignment: Chart choices fit the question, audience, and data types.
- Feasibility: Data and constraints support the plan.
- Clarity: Encodings and references are explicitly specified.
- Risk awareness: Assumptions and data limitations are identified.
Template 2 — Data Preparation to First Prototype
Purpose
Produce a minimal, reproducible visualization prototype from a dataset.
Inputs
- Dataset (or data source + access method)
- Selected chart strategy (from Template 1)
- Tool of choice (e.g., Tableau, Vega-Lite, Python, R)
Timebox
60–90 minutes
Procedure
1) Load and inspect
- Import data from a clean, documented path.
- Profile schema: variable names, types, ranges, missingness.
2) Tidy and transform
- Convert to tidy format as needed (one variable per column, one observation per row).
- Fix types (e.g., parse dates), handle missing values (document approach).
- Derive necessary fields (aggregations, rates, per-capita, bins).
3) Define mappings and scales
- Assign encodings: x/y position, length, color, size, shape.
- Configure scales: domains, zero baseline where appropriate (bar/area); log scale only with justification and positive values.
- Label units and time formats explicitly.
4) Build a minimal viable visualization (MVV)
- Implement the simplest version that answers the question.
- Add essential scaffolding: title, axis labels with units, legend or direct labels, data source credit.
- Apply a colorblind-safe palette and ensure label readability.
5) Annotate and export
- Add one-sentence takeaway or callout.
- Export static image (PNG/SVG) and save the reproducible script/workbook.
Checkpoints
- Reproducibility: The script/workbook runs from a clean session without manual steps.
- Data integrity: Types are correct; missing-data decisions are recorded.
- Visual correctness: Baselines, scales, and sorting are appropriate; labels and units are present.
- Accessibility basics: Text labels meet contrast recommendations (WCAG AA for text ≥ 4.5:1); palette remains distinguishable under color-vision deficiency simulation.
Deliverables
- Cleaned data snapshot or documented query (schema and data dictionary if applicable)
- Reproducible artifact (notebook, script, or workbook)
- MVV export (PNG/SVG) with title, labels, legend/annotations, and source note
- Brief methods note (data cleaning steps, encoding choices)
Evaluation Criteria
- Correctness: Data types, transformations, and encodings are accurate.
- Readability: Labels, units, and legends enable immediate interpretation.
- Reproducibility: Artifacts execute without hidden steps; dependencies are noted.
- Appropriateness: Chosen encodings match task and data type.
Template 3 — Quality Review, Iteration, and Communication Pack
Purpose
Evaluate a visualization for accuracy, clarity, accessibility, and integrity; iterate based on evidence; and package the final output for communication.
Inputs
- Prototype visualization and source artifacts (from Template 2)
- Chart Strategy Brief (from Template 1)
- Peer or user feedback (if available)
Timebox
45–75 minutes
Procedure
1) Heuristic quality review
- Perceptual effectiveness: Prefer position/length over angle/area/color for precise comparisons when possible.
- Integrity: Use a zero baseline for bar charts; avoid misleading aspect ratios; disclose sampling and transformations; avoid dual y-axes unless scales are unambiguous.
- Clarity: Reduce clutter; align text; standardize number formats; limit categories; ensure legible tick density.
- Accessibility: Use colorblind-safe palettes; ensure text contrast meets WCAG AA; provide meaningful alt text; do not encode solely by color.
- Statistical soundness: Show uncertainty where relevant; avoid implying causality from correlation; justify smoothing, binning, or filtering.
2) Stress and context tests
- Edge cases: Very long labels, extreme values, small n.
- Environment: Desktop/mobile/print legibility; dark/light theme issues.
- Performance: Rendering time and file size; interactivity responsiveness if applicable.
3) Gather feedback
- Conduct a quick task-based review with 2–3 peers: “What is the main takeaway?” “Find the top category.” Record time-to-answer and errors.
- Consolidate issues by severity and impact.
4) Iterate and document changes
- Prioritize fixes tied to success criteria.
- Update the visualization and notes; keep a change log listing issue, fix, and rationale.
5) Finalize communication assets
- Caption: 1–2 sentences stating the key insight and scope.
- Alt text: Concise, objective description of what the chart shows and how to read it.
- Methods summary: Data source, timeframe, key transformations, and limitations.
Checkpoints
- The visualization passes integrity checks (baselines, scales, labels, legends).
- Accessibility checks are satisfied (contrast, color redundancy, readable text).
- Peer testers can complete core tasks within the expected time and with low error.
- Changes are documented and traceable to issues and criteria.
Deliverables
- Before/after visualization exports
- Quality checklist with pass/fail notes and justifications
- Peer test notes (tasks, times, errors, comments)
- Change log (issue → action → outcome)
- Final visualization with caption, alt text, and methods summary
Evaluation Criteria
- Accuracy and integrity: No misleading encodings; assumptions disclosed.
- Clarity and usability: Viewers can accomplish primary tasks efficiently.
- Accessibility: Text contrast and color redundancy meet guidelines; alt text provided.
- Documentation: Clear rationale and traceable iteration history.
Use these templates in sequence (1 → 2 → 3) for end-to-end activities, or individually for targeted practice (e.g., only Template 3 for critique and iteration). Each template scales to different tools and datasets while maintaining consistent checkpoints and output expectations.
