Data Visualisation and Analytics Tools (Spreadsheets, Python)
Data visualisation turns analysis into a decision aid. The exam tests chart choice, spreadsheet mechanics and the Python ecosystem at a conceptual level.
Core rules
- Compare categories with bars; show trends over time with lines; show distribution with histograms or box plots; show relationship with scatter plots.
- Pie charts suit few parts of a whole; avoid them for many categories.
- Spreadsheets: use structured references, SUMIFS/COUNTIFS, pivot tables, XLOOKUP/INDEX-MATCH and charts; keep raw data separate from calculations.
- Python: pandas for cleaning and aggregation, matplotlib/seaborn for static plots, plotly for interactive dashboards.
- A good chart has a clear message, labelled axes, appropriate scale and no unnecessary decoration.
Worked example
A sales table has region, product and revenue. In a spreadsheet, a pivot table sums revenue by region; SUMIFS/COUNTIFS computes average order value by region. A bar chart compares regions, while a line chart tracks monthly trend. In pandas, the same task is df.groupby('region')['revenue'].sum(), followed by a bar plot. The calculation must reconcile to the trial total before the chart is trusted.
Traps
Do not truncate a bar chart’s y-axis to exaggerate differences; do not use a pie chart with twelve slices; do not chart raw counts when rates are needed; and do not hard-code ranges that omit new rows.