PART 2: Data Visualisation

PART 2: Data Visualisation

Data Visualization

Data Visualization focuses on representing data in clear, visually compelling formats to reveal patterns, trends, and insights. By mastering visualization principles and tools (e.g., Python, Excel, and Power BI), learners can transform complex datasets into meaningful graphics that support data exploration, enhance decision-making, and effectively communicate findings to diverse stakeholders. This section emphasizes developing practical skills, understanding ethical considerations, and creating accurate, impactful visuals aligned with professional standards.

Summarizing Data in Python

Learning Objectives

Apply different visualizations to summarize distributions and categorical data. Use Python libraries to generate informative graphs. This enables learners to efficiently explore datasets, identify patterns, and convey insights using well-structured graphical representations.

Indicative Content

  • Bar charts for categorical data:

    • Simple, stacked, and multiple bar charts using plot.bar().

  • Pie charts for proportions:

    • Creating pie charts with plot.pie().

  • Histograms for distribution analysis:

    • Using hist() to display frequency distributions.

  • Boxplots for spread and outliers:

    • Understanding quartiles and data spread using boxplot().

  • Density plots:

    • Visualizing probability distributions with plot.kde().

  • Heatmaps:

    • Representing intensity of data with sns.heatmap().

  • Pareto charts:

    • Combining bar and line graphs for cumulative impact analysis.

Visualizing Relationships in Python

Learning Objectives

Demonstrate how to visualize relationships between variables using different types of plots in Python. This will help learners analyze correlations, detect trends, and communicate complex relationships clearly through appropriate visual formats.

Indicative Content

  • Scatter plots and regression lines:

    • Using sns.lmplot() in Python to create scatter plots with regression lines.

    • Interpreting scatter plots in data visualization.

  • Scatter plot matrices:

    • Comparing multiple variables simultaneously using sns.pairplot().

  • Bubble charts:

    • Visualizing three variables using sns.scatterplot() with size encoding.

  • Trend lines:

    • Plotting trends over time with plt.plot().

  • Motion charts:

    • Creating interactive motion charts with plotly.express.scatter().

  • Advanced statistical plots such as violin plots or ridge plots could be included for additional analysis.

Tools and Methodologies

  • Python (e.g., matplotlib, seaborn, plotly) for creating various chart types (scatter, histogram, box, regression lines)

  • Excel for pivot tables, bar/pie charts, histograms, and interactive filtering via slicers

  • Power BI for building interactive dashboards, including KPI cards, slicers, and visual relationships

  • Methodologies

    • Summarize and explore distributions through fundamental visual forms (bar/pie charts, histograms, boxplots)

    • Visualize variable relationships via scatterplots (with or without regression lines), bubble charts, and multi-variable dashboards

    • Integrate ethical considerations (avoiding distorted scales, respecting data privacy) and maintain clarity, consistency, and transparency in all visuals