Intermediate Matplotlib: Data Visualization and Reporting in Uganda

Course outline and learning expectations

This is a tutor-led course. The outline shows what your tutor will cover; teaching materials and examinations are provided directly to enrolled students.

What you will learn

• By completing this intermediate Matplotlib course, learners will be able to:
• Build reusable visualizations using Matplotlib’s object-oriented interface.
• Design complex analytical layouts with subplots, GridSpec and subplot_mosaic.
• Visualize time-series, categorical, distributional and gridded data.
• Communicate uncertainty with error bars, confidence ranges and distribution plots.
• Create reusable organisational styles with style sheets and rcParams.
• Build professional, colour-accessible charts for reports and presentations.
• Develop reusable plotting functions for recurring reports.
• Automate the production of charts for multiple districts, indicators or reporting periods.
• Optimize complex visualizations for clarity, performance and file size.
• Export publication-quality charts in raster and vector formats.
• Produce an executive-ready visual report using Uganda or East Africa data.

Modules

1. 1

   Building a Professional Matplotlib Workflow

   Reviewing the Figure, Axes and Artist hierarchy.Moving from pyplot commands to object-oriented plotting.Separating data preparation from visualization code.Writing reusable plotting functions.Structuring notebooks and Python scripts for maintainability.Creating consistent naming, documentation and source notes.
2. 2

   Advanced Multi-Chart Layouts

   Designing complex subplot arrangements.Using GridSpec for flexible rows and columns.Creating semantic layouts with subplot_mosaic.Sharing axes, legends and colour scales.Building executive summary and analytical detail panels.Managing spacing with constrained and tight layouts.
3. 3

   Time-Series Visualization for African Data

   Working with dates and time indexes.Formatting date ticks and intervals.Comparing several time series without creating clutter.Highlighting policy changes, disruptions and significant events.Plotting moving averages and indexed trends.Visualizing inflation, exchange rates, commodity prices and operational performance.Preventing misleading comparisons between values with different scales.
4. 4

   Statistical Distributions and Uncertainty

   Advanced histograms and bin selection.Box plots, violin plots and empirical cumulative distributions.Error bars and uncertainty ranges.Comparing groups and regional distributions.Showing sample size and data limitations.Communicating uncertainty to non-technical decision-makers.Avoiding conclusions that the available data cannot support.
5. 5

   Heatmaps and High-Density Data

   Creating matrix and heatmap-style visualizations.Using imshow and pcolormesh.Designing meaningful colour scales and colour bars.Visualizing district-by-indicator and period-by-category data.Handling missing values in high-density charts.Deciding when a table is more useful than a chart.
6. 6

   Professional Styling and Organisational Branding

   Controlling appearance with rcParams.Building reusable .mplstyle files.Applying organisational fonts and colour palettes.Creating presentation, report and web variants.Designing accessible charts for print and digital distribution.Maintaining a consistent visual identity across recurring reports.
7. 7

   Advanced Annotation and Data Storytelling

   Using callouts and reference lines.Highlighting targets, thresholds and outliers.Positioning annotations using data and Axes coordinates.Creating direct labels that reduce legend dependence.Building a clear visual hierarchy.Turning analytical findings into decision-focused chart narratives.
8. 8

   Automated Reporting and Batch Chart Production

   Generating multiple charts with functions and loops.Producing district, branch, project or product-level reports.Creating dynamic titles, labels and filenames.Applying a consistent style across many outputs.Validating charts before publication.Managing missing, incomplete and unexpected data.Saving report-ready graphics automatically.
9. 9

   Performance, Export and Capstone Report

   Improving performance when plotting larger datasets.Using aggregation and sampling responsibly.Choosing between PNG, SVG and PDF.Controlling resolution, dimensions, transparency and file size.Preparing visuals for donor reports, policy briefs, academic publications and web pages.an executive visual report based on Uganda or East Africa economic, social, business or development data.