Module 1 | Introduction to Data Visualization
-Concepts, definitions, evolution, and relevance for development practitioners) | – Review of relevant videos on evolution and development of data visualization.
– Critique sample visuals |
Module 2 | Visual Perception and Cognitive Theory
-How the human brain interprets visuals; why this matters for effective visualization | – Working with the data to implement visual perception
– Color blindness checkers (tool is coblis)
– Practical application of cognition principle |
Module 3 | Why Visualization Matters in Development
-Role in design, planning, monitoring, evaluating, and communicating results of socio-economic programs | Developing visuals of program indicators across the program cycle (design, plan, monitor etc..) and communicating of results |
Module 4 | Types of Visualizations: Charts, Graphs, and When to Use Them
-Choosing the right visual based on data type, audience, and message | -Chart selection exercise using a chart chooser
-Simple dataset provided to participants to craft different visuals (using excel, google sheets, Tableau or datawrapper) |
Module 5 | Geospatial Visualization: Using Maps in Development Contexts
-Basics of GIS, when to use maps | -Demographic density maps
-Map facility data points using QGIS or Google My Maps |
Module 6 | Principles of Effective Visualization Design
-Best practices, dos and don’ts
-Data visualization checklist for quality control and consistency | -Redesign challenge (before and after). Improve charts using principles discussed in the morning
-Peer review of visuals using checklist |
Module 7 | Visualization Tools: Software and Technologies
-Overview of Google Looker, Excel, Power BI, Tableau, Datawrapper, Flourish | -Explore visualization tools.
Google Looker, Excel, Tableau,
Datawrapper and;
Report out |
Module 8 | Data Visualization for Storytelling and Influence
-Building narratives, humanizing data, visual storytelling principles
-Communicating with donors, communities, and policymakers | – Create simple dashboard using your own dataset or provided dataset
– Creating a story from visuals (storytelling) |
Module 9 | Common Pitfalls and How to Avoid Them
-Misleading visuals, chartjunk, overdesign, bias in color and scale | – Finding errors/mistakes in a visual
– Finding misleading chart elements
– Challenge to fix/correct charts |
Module 10 | AI Integration and use cases
-Introduction to AI tools (ChatGPT or Gemini) for quick chart summaries or dashboard narratives
-AI-powered insight summarization using Tableau GPT or Power BI Copilot
-AI-assisted GIS tools for predictive visualization (Google Earth Engine).
-Demo AI tools that auto-select the best chart type based on data (e.g., ChatGPT). | -Use an AI tool to summarize a data set
-Use an AI tool to generate a chart/visual
-Google Earth Engine demo |
