Acquire public datasets through freedom of information requests and structured web scraping.
Public Relations & Media
Data Journalism Techniques for Investigative Reporting
Acquire, clean and analyse public datasets, apply statistical and geospatial methods to uncover patterns, and present data-driven findings through visualisation and narrative for investigative stories.
🗓️Duration5 training days
📚Content4 modules · 8 sessions
🎓On completionAccredited attendance certificate
About the programme
Course Overview
A dataset does not become a story until it has been cleaned, checked and tested against alternative explanations for the pattern it appears to show. This course builds the practical skills of data journalism for investigative reporting: acquiring data through freedom of information requests and structured scraping, cleaning it into a reproducible format, and applying statistical and geospatial methods to separate genuine findings from data artefacts. Participants reproduce calculations independently, seek subject-matter review of methodology, and assess the privacy and defamation risk specific to publishing data that names organisations or could identify individuals within aggregated figures. Sessions on presentation cover choosing chart types that represent a finding accurately, designing interactive visualisations for non-specialist readers, and structuring a multi-part investigation so each instalment sustains interest while building toward the full picture. By the end, participants can take a raw public dataset through acquisition, analysis, verification and publication as a credible investigative story.
Expected Learning Outcomes
Clean and document raw data in a reproducible format suitable for fact-checking.
Apply statistical and geospatial methods to identify genuine patterns rather than data artefacts.
Verify data-driven findings through independent recalculation and subject-matter review.
Assess privacy, defamation and public interest risks specific to data-based reporting.
Design visualisations that represent findings accurately for non-specialist readers.
Structure a data-driven investigation into a narrative that sustains reader interest across instalments.
Who Should Attend
Investigative reporters working with public records and large datasets.
Newsroom data journalists supporting multiple reporting teams.
Freelance journalists building data analysis into investigative pitches.
Editors commissioning and reviewing data-driven investigative projects.
Public interest researchers transitioning into investigative journalism.
Communications staff at watchdog and non-profit organisations producing investigative reports.
Course Modules
Select any module to see its sessions and points.
01Sourcing and Preparing Data for Investigation
2 sessions · 8 points
Session 1Acquiring Data Through Public Records and Scraping
- File freedom of information requests structured to return usable datasets rather than narrative summaries.
- Use web scraping tools to extract structured data from public registers and government portals.
- Evaluate dataset provenance and update frequency before relying on it for a published claim.
- Cross-reference multiple public datasets to identify discrepancies worth investigating further.
Session 2Cleaning and Structuring Raw Data
- Clean inconsistent formatting, duplicate records and missing values using spreadsheet and scripting tools.
- Normalise data from multiple sources into a common structure for comparison and analysis.
- Document every cleaning step in a reproducible log that supports later fact-checking.
- Flag data quality limitations that must be disclosed alongside any published finding.
02Analysing Data for Patterns and Anomalies
2 sessions · 8 points
Session 1Applying Statistical Methods to Find the Story
- Use descriptive statistics and rate calculations to compare figures fairly across regions or time periods.
- Apply basic statistical significance checks before presenting a pattern as a meaningful finding.
- Identify outliers and investigate whether they reflect genuine anomalies or data errors.
- Build comparison baselines that put a raw number into context a reader can judge.
Session 2Geospatial and Network Analysis for Investigative Leads
- Map incident or transaction data geographically to reveal clustering not visible in a table.
- Use network analysis to trace relationships between entities across corporate registries and public filings.
- Combine geospatial layers with demographic data to test hypotheses about disproportionate impact.
- Validate spatial findings against ground-level reporting before treating them as confirmed.
03Verifying Findings Before Publication
2 sessions · 8 points
Session 1Fact-Checking Data-Driven Claims
- Reproduce a key calculation independently to confirm it before publication.
- Seek an independent statistical or subject-matter review of methodology for a high-stakes finding.
- Request comment from data-holding organisations with enough lead time for a genuine response.
- Document methodology transparently enough for another journalist to replicate the analysis.
Session 2Assessing Legal and Ethical Risk in Data Stories
- Review privacy implications of publishing data that could identify individuals within aggregated figures.
- Assess defamation risk when data implicates a named organisation or individual in wrongdoing.
- Balance public interest against potential harm when deciding how much raw data to publish alongside a story.
- Consult legal review on data obtained through leaks or contested access requests.
04Presenting Data-Driven Investigative Stories
2 sessions · 8 points
Session 1Visualising Findings for Non-Specialist Readers
- Choose chart types that represent the finding accurately rather than for visual impact alone.
- Design interactive visualisations that let readers explore data relevant to their own location or interest.
- Avoid visual choices that exaggerate small differences or obscure important context.
- Pair every visualisation with a caption explaining the data source and any limitations.
Session 2Structuring the Narrative Around the Data
- Build a narrative structure that leads with human impact before introducing supporting data.
- Sequence a multi-part investigation so each instalment stands alone while building toward the full picture.
- Write methodology notes and published datasets that support the story's credibility with expert readers.
- Plan follow-up reporting triggered by reader tips or new data released after initial publication.
What the participant receives
4 course modules
A structured syllabus
8 training sessions
across 5 days
32 detailed points
Applied, detailed content
Accredited attendance certificate
On completing the programme
Complete your registration
We will contact you within one business day to confirm.
Ready to start?
Reserve your seat and start building the skill.