Data Journalism & Visual Storytelling is a structured, practical course covering Data journalism, D3.js, Datawrapper, Flourish, Narrative. It emphasizes explainable methods, checked work and a final outcome that can be reviewed.
Policy & civic techTool stack
D3
Technology marks for Data Journalism & Visual Storytelling, sourced from the CC0-licensed Simple Icons project.
Use Data journalism appropriately in a realistic, bounded task.
Use D3.js appropriately in a realistic, bounded task.
Use Datawrapper appropriately in a realistic, bounded task.
Use Flourish appropriately in a realistic, bounded task.
Course outline and revision prompts
Module 1
Data journalism: Public problems
Apply Data journalism through stakeholders, institutions, evidence.
Lesson
Key terms
Revision question
Stakeholders with Data journalism
stakeholders, Data journalism, policy
In Data Journalism & Visual Storytelling, which evidence best supports a stakeholders result produced with Data journalism?
Institutions with D3.js
institutions, D3.js, policy
In Data Journalism & Visual Storytelling, which evidence best supports a institutions result produced with D3.js?
Evidence with Datawrapper
evidence, Datawrapper, policy
In Data Journalism & Visual Storytelling, which evidence best supports a evidence result produced with Datawrapper?
Instructional figureDecision matrix
Stakeholders: from context to evidence
Stakeholders connects public need and evidence to a accountable outcome in Data Journalism & Visual Storytelling.
1Public need and evidence
Define the purpose, intended user and Data journalism constraints.
frames
2Stakeholders
Map who decides, implements, benefits, pays and challenges.
produces evidence for
3Accountable outcome
Compare the observed result with a normal case, boundary case and stated limitation.
Takeaway: Stakeholders is credible only when the result can be traced back to its purpose, inputs and constraints. Stakeholders is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 2
D3.js: Digital methods
Apply D3.js through open data, services, participation.
Lesson
Key terms
Revision question
Open data with Flourish
open data, Flourish, policy
In Data Journalism & Visual Storytelling, which evidence best supports a open data result produced with Flourish?
Services with Narrative
services, Narrative, policy
In Data Journalism & Visual Storytelling, which evidence best supports a services result produced with Narrative?
Participation with Data journalism
participation, Data journalism, policy
In Data Journalism & Visual Storytelling, which evidence best supports a participation result produced with Data journalism?
Instructional figureContinuous cycle
Open Data: from context to evidence
Open Data connects public need and evidence to a accountable outcome in Data Journalism & Visual Storytelling.
1Public need and evidence
Define the purpose, intended user and Flourish constraints.
frames
2Open Data
Check licence, provenance, updates and disclosure risk.
produces evidence for
3Accountable outcome
Compare the observed result with a normal case, boundary case and stated limitation.
Review the evidence, adjust the method, and repeat.
Takeaway: Open Data is credible only when the result can be traced back to its purpose, inputs and constraints. Open Data is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 3
Datawrapper: Policy design
Apply Datawrapper through options, impact, implementation.
Lesson
Key terms
Revision question
Options with D3.js
options, D3.js, policy
In Data Journalism & Visual Storytelling, which evidence best supports a options result produced with D3.js?
Impact with Datawrapper
impact, Datawrapper, policy
In Data Journalism & Visual Storytelling, which evidence best supports a impact result produced with Datawrapper?
Implementation with Flourish
implementation, Flourish, policy
In Data Journalism & Visual Storytelling, which evidence best supports a implementation result produced with Flourish?
Instructional figureSide-by-side comparison
Options: from context to evidence
Options connects public need and evidence to a accountable outcome in Data Journalism & Visual Storytelling.
1Public need and evidence
Define the purpose, intended user and D3.js constraints.
frames
2Options
Include capacity, distribution and a no-action baseline.
produces evidence for
3Accountable outcome
Compare the observed result with a normal case, boundary case and stated limitation.
Takeaway: Options is credible only when the result can be traced back to its purpose, inputs and constraints. Options is the decision layer between the starting context and evidence that the result is fit for purpose.
Module 4
Flourish: Accountability
Apply Flourish through measurement, transparency, maintenance.
Lesson
Key terms
Revision question
Measurement with Narrative
measurement, Narrative, policy
In Data Journalism & Visual Storytelling, which evidence best supports a measurement result produced with Narrative?
Transparency with Data journalism
transparency, Data journalism, policy
In Data Journalism & Visual Storytelling, which evidence best supports a transparency result produced with Data journalism?
Maintenance with D3.js
maintenance, D3.js, policy
In Data Journalism & Visual Storytelling, which evidence best supports a maintenance result produced with D3.js?
Instructional figureContinuous cycle
Measurement: from context to evidence
Measurement connects public need and evidence to a accountable outcome in Data Journalism & Visual Storytelling.
1Public need and evidence
Define the purpose, intended user and Narrative constraints.
frames
2Measurement
Avoid vanity indicators and report distribution.
produces evidence for
3Accountable outcome
Compare the observed result with a normal case, boundary case and stated limitation.
Review the evidence, adjust the method, and repeat.
Takeaway: Measurement is credible only when the result can be traced back to its purpose, inputs and constraints. Measurement is the decision layer between the starting context and evidence that the result is fit for purpose.
Course practical outcome
Produce a reviewable Data Journalism & Visual Storytelling project using Data journalism, D3.js, Datawrapper.
Expected output: A working Data Journalism & Visual Storytelling artefact plus an evidence-based self-review.
Tools: A suitable Data journalism environment, A plain-text decision log, Test data or realistic sample material
Production steps
Define the intended user, outcome and constraints.
Create the smallest complete result using Data journalism.
Test one normal case, one boundary case and one failure response.
Revise the work from the evidence and preserve before-and-after results.
Prepare a concise handover containing method, limitations and next step.
Success criteria
The output matches the stated outcome.
Inputs and decisions are reproducible.
Boundary and failure evidence is included.
Limitations and responsibility considerations are explicit.
Self-review
Can another learner repeat the method?
Did I test a difficult case?
Did I avoid unsupported claims?
Is the next action proportionate to the remaining risk?
Next step: Choose one weakness found during review and improve it before extending the Data journalism scope.
Glossary
Data journalism
A core concept or tool used in Data Journalism & Visual Storytelling; its exact meaning is established in the relevant lesson.
D3.js
A core concept or tool used in Data Journalism & Visual Storytelling; its exact meaning is established in the relevant lesson.
Datawrapper
A core concept or tool used in Data Journalism & Visual Storytelling; its exact meaning is established in the relevant lesson.
Flourish
A core concept or tool used in Data Journalism & Visual Storytelling; its exact meaning is established in the relevant lesson.
Narrative
A core concept or tool used in Data Journalism & Visual Storytelling; its exact meaning is established in the relevant lesson.
This guide is generated from DigiLearn course material. Product versions, regulations and professional standards can change; consult the linked authoritative source before applying version-sensitive guidance.