Make charts that tell the checked truth

Before you begin

• Python onramp; inspect the worked fixture before beginning.
• A text editor/spreadsheet for the manual route. Hosted Python/Jupyter for code exercises; applicable dependencies are in requirements.txt.
• For actual AI calls, a legitimate available cloud service and authorised learner credentials; no particular provider or country is mandatory.

Free original, non-accredited practice. Self-review is not independent assessment. No particular provider, payment platform or country is mandatory; use a legitimate available route or explicitly labelled manual practice.

Choose a question and chart type

A chart is an argument about data. Bar lengths need a meaningful baseline, units and readable labels. Accessibility also needs a text alternative, not simply a contrast check. Inspect a deliberately truncated-axis specification before rendering a corrected chart. Match the encoding to comparison, distribution or change. Inspect the supplied input before using any tool. Write the decision rules in your own words and calculate one small result independently. The reference output is an answer key for this deliberately bounded fixture, not proof of general capability. Policy for this project: Values are fictional counts. A bar chart must start at zero for this exercise and include units and a text alternative. Metadata checks cannot prove the rendered chart is visually honest. For learners across regions underserved or excluded by mainstream providers: verify country eligibility, language, account/identity requirements, data terms, cost, quota and connectivity before choosing a cloud service. Use only legitimate available alternatives; never bypass restrictions or invent a country/account. No service access? Complete the labelled manual/fixture route without claiming a live AI run.

Worked example

SOURCE (authored fixture):
{
  "values": {
    "A": 8,
    "B": 10
  },
  "chart": {
    "axis_min": 7,
    "units": "items",
    "alt": ""
  }
}
INDEPENDENT FIXTURE TARGET:
{
  "total": 18,
  "failed_checks": [
    "axis_baseline",
    "text_alternative"
  ],
  "release": "revise"
}

Your practical task

1. Match the encoding to comparison, distribution or change.
2. Open input.json and expected.json from the practice pack; keep an unmodified original.
3. Record a manual prediction and the relevant rule before consulting the answer key.
4. Perform one small change and predict its effect before rerunning.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Choose a question and chart type — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Set baseline to 0 and add a descriptive text alternative: specification checks pass.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Audit the supplied misleading spec

A chart is an argument about data. Bar lengths need a meaningful baseline, units and readable labels. Accessibility also needs a text alternative, not simply a contrast check. Inspect a deliberately truncated-axis specification before rendering a corrected chart. Identify what a truncated baseline exaggerates. Establish a baseline before changing prompts or code. Keep the original input immutable. Record expected values, observed values and whether each comparison passed. If the expected file conflicts with a justified policy, investigate rather than forcing a convenient match. Policy for this project: Values are fictional counts. A bar chart must start at zero for this exercise and include units and a text alternative. Metadata checks cannot prove the rendered chart is visually honest.

Worked example

MANUAL BASELINE / compare every named field
{
  "total": 18,
  "failed_checks": [
    "axis_baseline",
    "text_alternative"
  ],
  "release": "revise"
}
Keep a separate observed-result column; the answer key is not a model response.

Your practical task

1. Identify what a truncated baseline exaggerates.
2. Open input.json and expected.json from the practice pack; keep an unmodified original.
3. Record expected and observed values separately; explain every mismatch.
4. Perform one small change and predict its effect before rerunning.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Audit the supplied misleading spec — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Swap category labels: values may total correctly while the chart is still wrong.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Render an accessible chart

A chart is an argument about data. Bar lengths need a meaningful baseline, units and readable labels. Accessibility also needs a text alternative, not simply a contrast check. Inspect a deliberately truncated-axis specification before rendering a corrected chart. Use clear units, labels and a checked table alongside the image. Build one inspectable transformation or bounded AI prompt. Keep calculation and validation deterministic where possible. If you make a permitted hosted call, save the actual response separately from the authored examples and verify its material claims; the supplied reference never simulates a live model. Policy for this project: Values are fictional counts. A bar chart must start at zero for this exercise and include units and a text alternative. Metadata checks cannot prove the rendered chart is visually honest.

Worked example

PROJECT CONTRACT:
Values are fictional counts. A bar chart must start at zero for this exercise and include units and a text alternative. Metadata checks cannot prove the rendered chart is visually honest.
HOSTED REFERENCE: open hosted-lab.ipynb or run reference.py with all fixture files together.
EXPECTED CONSTRUCTED RESULT:
{
  "total": 18,
  "failed_checks": [
    "axis_baseline",
    "text_alternative"
  ],
  "release": "revise"
}

Your practical task

1. Use clear units, labels and a checked table alongside the image.
2. Open hosted-lab.ipynb and the reference code from the practice pack; keep an unmodified original.
3. Record expected and observed values separately; explain every mismatch.
4. Perform Colour alone must not distinguish categories.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Render an accessible chart — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Colour alone must not distinguish categories.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Test misleading transformations

A chart is an argument about data. Bar lengths need a meaningful baseline, units and readable labels. Accessibility also needs a text alternative, not simply a contrast check. Inspect a deliberately truncated-axis specification before rendering a corrected chart. Try swapped labels, omitted rows and changed scales. Change one assumption at a time and predict the result first. Retain the failure, the revision and the rerun evidence. A failure is useful information: hiding a bad output or editing a test target after seeing the answer destroys the value of the experiment. Policy for this project: Values are fictional counts. A bar chart must start at zero for this exercise and include units and a text alternative. Metadata checks cannot prove the rendered chart is visually honest.

Worked example

CHANGE TESTS — predict before running:
1. Set baseline to 0 and add a descriptive text alternative: specification checks pass.
2. Swap category labels: values may total correctly while the chart is still wrong.
3. Colour alone must not distinguish categories.
Keep the original expected.json unchanged.

Your practical task

1. Try swapped labels, omitted rows and changed scales.
2. Open failure-tests.md from the practice pack; keep an unmodified original.
3. Record expected and observed values separately; explain every mismatch.
4. Perform Set baseline to 0 and add a descriptive text alternative: specification checks pass.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Test misleading transformations — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Set baseline to 0 and add a descriptive text alternative: specification checks pass.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Write a factual text alternative

A chart is an argument about data. Bar lengths need a meaningful baseline, units and readable labels. Accessibility also needs a text alternative, not simply a contrast check. Inspect a deliberately truncated-axis specification before rendering a corrected chart. Describe the observed values without inventing an explanation. Review what the fixture cannot establish. Explain who owns the decision, where human approval is needed and which access or professional constraints remain. Do not describe a small educational success as production readiness, certification, legal compliance or a guarantee. Policy for this project: Values are fictional counts. A bar chart must start at zero for this exercise and include units and a text alternative. Metadata checks cannot prove the rendered chart is visually honest. For learners across regions underserved or excluded by mainstream providers: verify country eligibility, language, account/identity requirements, data terms, cost, quota and connectivity before choosing a cloud service. Use only legitimate available alternatives; never bypass restrictions or invent a country/account. No service access? Complete the labelled manual/fixture route without claiming a live AI run.

Worked example

REVIEW BOUNDARY:
Values are fictional counts. A bar chart must start at zero for this exercise and include units and a text alternative. Metadata checks cannot prove the rendered chart is visually honest.

ACCESS / NOT TESTED: unavailable provider, unsupported language and absent external feedback must remain explicit.

Your practical task

1. Describe the observed values without inventing an explanation.
2. Open README.md and your evidence log from the practice pack; keep an unmodified original.
3. Record the human decision owner, limitations and any untested service access.
4. Perform one small change and predict its effect before rerunning.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Write a factual text alternative — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Swap category labels: values may total correctly while the chart is still wrong.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Deliver a chart and decision brief

A chart is an argument about data. Bar lengths need a meaningful baseline, units and readable labels. Accessibility also needs a text alternative, not simply a contrast check. Inspect a deliberately truncated-axis specification before rendering a corrected chart. Include source, code, accessible description and limitations. Deliver a reproducible bundle rather than a screenshot alone. Include inputs, rules, output, failure tests, a short README and residual limitations. Ask another person to follow the instructions if possible; self-review and actual external review must be labelled separately. Policy for this project: Values are fictional counts. A bar chart must start at zero for this exercise and include units and a text alternative. Metadata checks cannot prove the rendered chart is visually honest.

Worked example

HANDOFF MANIFEST:
input.json — immutable source
expected.json — fixture answer key
actual.json — result actually observed
evidence-log.csv — baseline plus three changes
README.md — reproduction and access route
limitations.md — unresolved failures, rights, approvals and scope
Never bundle a credential.

Your practical task

1. Include source, code, accessible description and limitations.
2. Open README.md and your evidence log from the practice pack; keep an unmodified original.
3. Record the human decision owner, limitations and any untested service access.
4. Perform one small change and predict its effect before rerunning.
5. Keep the result and your reasoning in the lesson notes; do not mark a simulated run as live.

Evidence to keep

• Deliver a chart and decision brief — a saved, inspectable artefact tied to the supplied rule.
• A baseline or change-test record with expected/observed results and a limitation.

Mistakes to watch for

• Colour alone must not distinguish categories.
• Treating a supplied answer key, an authored example or self-review as independent field validation.

Check your understanding

Which exact input, rule and expected value would let another learner falsify your conclusion in this activity, and what remains untested?

No-service / offline route

Use the supplied fictional JSON, expected results and paper/text evidence log without a provider account. Manual or constructed completion is labelled; it is not a live cloud integration or trained-model run.

Capstone self-review

Score each criterion 0 (missing), 1 (partial), or 2 (demonstrated). A self-review pass needs at least 8/10 and 2/2 on the critical safety/limits criterion. This is learner practice feedback, not verified assessment.

• Defined task and preserved input: Original input and the project policy are retained; the actual question is clear.
• Checked baseline: The supplied baseline reconciles field by field; unsupported live-run claims are absent.
• Three failure/change tests: All three specified changes have predictions, observed results and explanations.
• Repeatable handoff: Another person can follow the README; supporting practice evidence and unresolved limits are included.
• Safety, rights and access honesty — critical: Values are fictional counts. A bar chart must start at zero for this exercise and include units and a text alternative. Metadata checks cannot prove the rendered chart is visually honest. Regional access route is legitimate; credentials and private data are absent; manual/constructed work is clearly labelled.

Review questions

Practice files

• README.md
• evidence-log.csv
• expected.json
• failure-tests.md
• hosted-lab.ipynb
• input.json
• lab_engine.py
• reference.py
• requirements.txt

References and editorial limits

Original authored practice material; deterministic fixtures build-tested. Not independently expert-reviewed or learner-validated. Time estimates unvalidated.

• Matplotlib accessibility guidance

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