# Types of Data Visualization Techniques

Data Visualization is a critical aspect of data science and analytics. It refers to presenting data in graphical or visual formats, such as charts, graphs, and maps, to help people understand the patterns, trends, and insights hidden within large and complex datasets. There are various data visualization techniques that you should know as a Data Science professional. In this article, I’ll take you through types of data visualization techniques and the types of charts and graphs you can use in each technique.

## Types of Data Visualization Techniques

There are various types of data visualization techniques for each specific data characteristic and analytical objectives. Data Science professionals should have a comprehensive understanding of all data visualization techniques. Below are some of the most valuable data visualization techniques you should know:

1. Comparison
2. Composition
3. Distribution
4. Trends over Time
5. Relationships
6. Geospatial
7. Hierarchical

Now, let’s go through all these techniques in detail.

#### Comparison

Comparison involves representing data in a way that allows you to easily compare different data points or categories. It aims to show the relative differences or similarities between data elements.

Charts you can use for visualizing comparisons are:

1. Bar Charts: Bar charts compare discrete categories or items. They display data as rectangular bars, with the length or height of each bar representing the quantity or value associated with the category.
2. Pie Charts: Pie charts can show the composition of a whole, making it easy to compare the proportions of different parts.

#### Composition

Composition focuses on depicting the parts that make up a whole. It is used to show the relative contribution of different elements to a total or to understand the makeup of a dataset.

Charts you can use for visualizing composition are:

1. Pie Charts: Pie charts represent parts of a whole, showing the proportion of each category as slices of a circle. They are useful for illustrating the composition of a dataset.
2. Stacked Bar Charts: Stacked bar charts display the composition of a whole by stacking segments within each bar or column. They show how different categories contribute to a total.

#### Distribution

Distribution is a technique used to understand the spread or pattern of data values. It helps in identifying trends, central tendencies, and outliers.

Charts you can use for visualizing distribution are:

1. Histograms: Histograms are used to visualize the distribution of continuous data by dividing it into bins or intervals and plotting the frequency or count of data points in each bin.
2. Box Plots (Box-and-Whisker Plot): Box plots provide a summary of the data’s distribution, showing the median, quartiles, and potential outliers. They are particularly effective for comparing distributions across categories.
3. Density Plots: Density plots, like kernel density plots, display the probability density of continuous data. They offer insights into the data’s underlying distribution.

#### Trends over Time

This technique focuses on displaying how data points change or evolve over a period of time. It is crucial for tracking patterns, identifying seasonality, and making forecasts.

Charts you can use for visualizing trends over time are:

1. Line Charts: Line charts are employed to depict trends and changes over time. They connect data points with lines, making them suitable for showing time series data or trends.
2. Area Charts: Area charts are similar to line charts but fill the area beneath the line, making it easier to discern the magnitude of change over time.

#### Relationships

Relationships are used to explore connections or associations between different variables in a dataset. It helps in identifying correlations and patterns.

Charts you can use for visualizing relationships are:

1. Scatter Plots: Scatter plots illustrate the relationship between two continuous variables. Each point represents a data point, with one variable on the x-axis and the other on the y-axis. They are used to identify correlations or patterns.
2. Correlation Heatmaps: Heatmaps can reveal the strength and direction of correlations between multiple variables.

#### Geospatial

Geospatial involves representing data on maps to convey information about location, geography, and spatial patterns.

Charts you can use for visualizing geospatial data are:

1. Choropleth Maps: Choropleth maps use colour gradients to represent data values in different geographic regions. They help visualize regional variations or distributions.
2. Marker Maps: Marker maps display individual data points on a map, such as store locations or earthquake epicentres.

#### Hierarchical

Hierarchical is used to represent data that has a hierarchical structure, where elements are organized in parent-child relationships.

Charts you can use for visualizing hierarchical data are:

1. Tree Maps: Tree maps display hierarchical data structures using nested rectangles. They show how larger categories break down into smaller subcategories.
2. Sunburst Charts: Sunburst charts are circular hierarchical visualizations that convey the structure of data hierarchies.

I hope this was helpful in understanding the types of data visualization techniques and the charts used in each technique.

### Summary

So, below are some of the most valuable data visualization techniques you should know:

1. Comparison
2. Composition
3. Distribution
4. Trends over Time
5. Relationships
6. Geospatial
7. Hierarchical

I hope you liked this article on the types of Data Visualization techniques. Feel free to ask valuable questions in the comments section below.

##### Aman Kharwal

I'm a writer and data scientist on a mission to educate others about the incredible power of dataðŸ“ˆ.

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