PART 2: DATA VISUALISATION

PART 2: DATA VISUALISATION

Fundamentals of Data Visualisation

Learning Objectives

Explain the significance of data visualization and identify key principles for effective visual representation. Compare different types of visualizations and evaluate the suitability of various tools. The goal is to ensure learners understand how visualization improves data interpretation, aids in decision-making, and supports effective communication in data-driven environments.

Indicative Content

  • Definition and importance of data visualization:

    • Enhancing comprehension, pattern recognition, and decision-making.

    • Identifying trends, correlations, and distributions in data.

  • Core Principles of Effective Visualization

    • ACCENT framework: Apprehension, Clarity, Consistency, Efficiency, Necessity, and Truthfulness.

    • Choosing the right chart type (bar, line, scatter, boxplot, histogram)

    • Color theory, labeling, avoiding misleading scales (chart “junk”)

    • Storytelling vs. mere plotting: presenting insights clearly to various stakeholders

  • Data Storytelling & Communication

    • Structuring a data narrative (focusing on key insights and takeaways)

    • Identifying your audience and tailoring visuals accordingly

    • Common pitfalls (e.g., misrepresenting data, incomplete context)

  • Tools:

    • Python: matplotlib, seaborn, plotly for programmatic visualization.

    • Excel: PivotTables, bar charts, pie charts, treemaps.

    • Power BI: Dashboards, slicers, KPI cards, and interactive reports.

    • Basic interactive features (hover, tooltips) if applicable

    • (Optional mention) Other popular tools or frameworks (e.g., D3.js, R/ggplot2)