Applied Data Visualisation Mastery with Python

Ideal For

This course is designed for:

• Data analysts and business intelligence professionals who produce regular reports and dashboards and want to elevate the quality, clarity, and impact of their visual outputs

• Data scientists who can build models but want to communicate their findings more effectively to non-technical stakeholders and business leaders

• Product managers and business analysts who work with data regularly and need to present insights compellingly to executives, clients, and investors

• Financial analysts and economists working with large datasets who want to move beyond Excel charts into professional Python-powered visualisation

• Researchers and academics who need publication-quality charts and figures for papers, reports, and presentations

• Marketing and growth professionals who work with campaign data, customer analytics, and market research and want richer, more persuasive visual outputs

• Journalists and communications professionals working in data journalism who want to build interactive and geospatial visualisations for digital publishing

• Anyone who regularly presents data to an audience and wants their work to land with clarity, credibility, and visual impact

Recommended prior knowledge: Basic Python familiarity — ability to write simple scripts and work with variables and lists. No prior data visualisation experience required.

What Participants will learn

By the end of this course, participants will be able to:

• Apply core data visualisation design principles — including colour theory, chart selection, visual hierarchy, and accessibility — to produce charts that communicate clearly and accurately to any audience

• Build and fully customise every major chart type using Matplotlib — including multi-plot layouts, annotations, custom themes, and publication-quality export settings

• Conduct and communicate a complete exploratory data analysis using Seaborn's statistical visualisation tools — including distribution plots, categorical comparisons, correlation heatmaps, and regression visualisations

• Build fully interactive charts, animated time-series visualisations, and advanced chart types using Plotly Express and Plotly Graph Objects

• Design and deploy multi-page interactive web dashboards using Plotly Dash — with real-time user controls, live data connections, and custom styling — accessible via a live web URL

• Create interactive geospatial maps and choropleth visualisations of Nigerian and African data using Folium and Plotly's mapping capabilities

• Structure and deliver a complete data story — from raw dataset to executive-ready visual narrative — using the single-insight principle, annotation strategies, and presentation design techniques

• Select the right visualisation tool and chart type for any data type, audience, and communication goal — and justify that selection with design reasoning

• Build a professional visualisation portfolio demonstrating proficiency across static reporting, interactive dashboarding, geospatial mapping, and data storytelling

• Export and integrate Python visualisations into PowerPoint, Google Slides, and web publishing formats for professional stakeholder communication

Course Description

Participants will learn to create stunning, publication-quality visualisations using Matplotlib, Seaborn, and Plotly — building interactive dashboards, geospatial maps of African data, and compelling data narratives that communicate insight clearly and persuasively to any audience. Hands-on from the first session.