KodeKloud Notes

Data visualization plays a crucial role in turning raw numbers into actionable insights. Depending on the story you want to tell, you can choose from four reporting styles:

Descriptive: Record what happened.
Diagnostic: Explain why it happened.
Predictive: Forecast what will happen next.
Prescriptive: Recommend actions to influence future outcomes.

The image outlines four kinds of reporting: Descriptive, Diagnostic, Predictive, and Prescriptive, each with a brief explanation of its focus.

1. Descriptive Visualization: Tables

A table is the simplest way to display raw data. It lists exact values in rows and columns, making it ideal for descriptive analytics.

The image shows a table listing first names, last names, and order dates, with a note about common visualizations and a button labeled "Descriptive."

2. Diagnostic Visualization: Bar & Column Charts

Bar and column charts excel at comparing categories side by side, helping you spot patterns and outliers quickly.

Chart Type	Use Case	Example
Bar Chart (Horizontal)	Compare single measure across categories	Shipping fees by city
Column Chart (Vertical)	Track changes over time or categories	Monthly sales
Clustered Column Chart	Compare two measures in each category	Shipping fees vs. taxes by city
Stacked Column Chart	Show part-to-whole across categories	Expense breakdown by department
100% Stacked Column Chart	Normalize categories to 100% for contribution	Market share by region

The image shows four bar and column charts comparing shipping fees and taxes by city, labeled under "Common Visualizations: Bars, Columns." It includes a "Diagnostic" button and the phrase "Compare values."

3. Trends Over Time: Line & Waterfall Charts

Line Graph: Tracks continuous data points over time to reveal trends and seasonality.
Waterfall Chart: Illustrates incremental changes, highlighting contributions to overall growth or decline.

The image shows examples of common visualizations, including a line graph and a waterfall graph, used to observe changes over time. It highlights the concepts of diagnostic and predictive analysis.

4. Geographic Distribution: Map Visualizations

Map charts combine spatial data with metrics (bubble size or color intensity) to reveal regional patterns. They start as descriptive but can become diagnostic when clusters emerge.

The image shows a map of North America with blue circles indicating the sum of shipping fees by city. It is labeled as a common chart type for geographical distribution, with options for descriptive or diagnostic analysis.

5. Proportional Analysis: Pie Charts & Tree Maps

Pie Chart: Compares parts to a whole.
Tree Map: Uses nested rectangles to represent hierarchical proportions, making it easier to see smaller segments.

Best Practice

Limit pie charts to 5–7 slices for clarity. Too many segments make the chart hard to read.

The image shows two types of data visualizations: a pie chart and a tree map, both representing the sum of shipping fees by ship city.

6. Relationship Analysis: Scatter Charts

Scatter plots reveal correlations, clusters, and outliers across two measures:

Predictive: A tight cluster along a trend line enables accurate forecasting.
Diagnostic: Multiple clusters point to distinct subgroups—compare slopes to understand their behavior.
Prescriptive: Outliers highlight risks or opportunities that warrant further action.

Warning

Watch out for overplotting when you have many points—consider transparency or binning to preserve insight.

The image shows three scatter charts illustrating different types of data relationships: predictive, diagnostic, and prescriptive, with trend lines and outliers.

Links and References

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