ICT for Civic Data
Crash Course 2026
Using data and AI to communicate ideas — from reading an RFP to building a data-backed proposal with maps, charts, and a published data portal.
Self-Paced Review
Thematic handout decks covering the full course content, organised for independent study. Each section follows a consistent structure: what are we trying to do, what we need to know, how to do it, why we did it this way, and Q&A.
Foundations
The data pipeline, prompt framework, core principles that underpin everything.
→A: Reading an RFP
Understanding the funder, the 12 analysis questions, show don't tell.
→B: From Example to Proposal
Case study to angle, tangible deliverables, project roles.
→C: Finding & Getting Data
Data sources, APIs, prompts for discovery and extraction.
→D: Verification & Cleaning
Google Sheets techniques, OpenRefine, formulas, cleaning principles.
→E: Enrichment & Combination
Combining datasets, granularity, the health facilities exercise.
→F: Visualisation & Presentation
Chart types, Chart.js, landing pages, v0, skills, data privacy.
→G: Storytelling & Peer Review
From tools to storytelling, the peer review framework.
→Course Materials
Day-by-day slides as presented in class. Use these to review what was covered each day.
Day 1: Project Definition
RFP analysis, choosing your angle, team formation, first map on GitHub Pages.
→Day 2: From Data to Map
FloodArchive exercise, precise prompting, data formats and sources, individual data work.
→Day 3: Enrichment & Cleaning
Health facility enrichment, prompt framework, Google Sheets cleaning, OpenRefine.
→Day 4: Analysis & Storytelling
Categorisation, Chart.js dashboards, landing pages, v0, skills, peer review.
→Day 5: Refining Your Story
Dataviz principles, individual refinement, peer review, exam briefing, retrospective.
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