# 10 Types of Data Visualization: From Basic to Advanced

By NIIT Editorial

Published on 26/06/2023

Data visualisation is the practise of making data and information more easily understood and used for decision-making via the creation of visual representations of data and information. It's an effective method for discovering, analysing, and sharing information with others.

This article aims to provide a broad introduction to data visualisation by discussing its value, common practises, and available resources. We will also talk about how to think about your target demographic, the quality of your data, and basic design principles while making a data visualisation. This essay will assist everyone, from data analysts to company executives to students, see the value of data visualisation and learn how to use it correctly.

• Common Types of Data Visualization
• Advanced Types of Data Visualization
• Comparison of Types of Data Visualization
• Conclusion

Common Types of Data Visualization

1. Bar Graphs

In a bar graph, each bar's height corresponds to the value it displays. Each bar's height corresponds to the numerical value it displays.

• When to Use: Bar graphs are excellent for comparing numbers that fall into distinct categories or groupings, such as sales by location or students by grade.

• Examples: A bar chart might display the monthly sales of a car lot or the income earned by several product lines.

2. Line Graphs

To show values across time or other continuous variables, a line graph draws lines between data points.

• When to Use: Data trends or patterns over time or other continuous variables may be effectively shown using line graphs.

• Examples: A line graph might display weekly temperature swings or annual sales growth for a business.

3. Pie Charts

A pie chart, often known as a circular bar graph with equal segments or "slices," is a graphical representation of quantitative data.

• When to Use: Percentages and other fractions, such as the market share of various brands, may be shown clearly and concisely using pie charts.

• Examples: A pie chart might be used to illustrate the breakdown of sales by department or the breakdown of the population by age bracket.

4. Scatter Plots

Each dot in a scatter plot represents the value of two variables, and the plot itself is a representation of the relationship between those variables.

• When to Use: Using a scatter plot, one may see the association between temperature and ice cream sales, for example.
• Examples: The link between unemployment rates and inflation, for example, or the relationship between the height and weight of pupils at a certain school, may be visualised using a scatter plot.

5. Heat Maps

A heat map is a kind of bar graph in which different colours are used to denote different values. The colours are often organised on a scale from dark to light.

• When to Use: Heat maps are effective for visualising trends and patterns in massive datasets, such as those describing website traffic or consumer demographics.

• Examples: Crime statistics throughout a city, or website traffic patterns, might both be visualised using a heat map.

1. Treemaps

To display hierarchical information, a treemap employs a chart with stacked rectangles. Each rectangle's size is proportional to the significance of the information it displays.

• When to Use: Treemaps are an effective way to display the structure and relative sizes of several classes or groupings, such as product lines or market shares.

• Examples: A treemap might display the distribution of various sorts of crime across a city, for example, or the distribution of a company's money among its many divisions.

2. Network Diagrams

A network diagram is a kind of relationship chart consisting of nodes and edges to show how things are connected to one another. Entities are represented by nodes, and the links between them by edges.

• When to Use: Social networks, transportation networks, and supply chains are all examples of systems that benefit greatly from network visualisations.

• Examples: Social media connections, intercity travel patterns, and other data may all be represented visually using network diagrams.

3. Chord Diagrams

Chord diagrams are circular charts where arcs are used to depict relationships between various nodes. Each arc's breadth reflects the depth of the bond.

• When to Use: Chord diagrams are helpful for depicting the interdependencies and interconnections between various entities, such as the transfer of resources or information between nations or businesses.

• Examples: Chord diagrams may be used to display everything from international commercial ties to internal organisational networks.

5. Sankey Diagrams

A Sankey diagram is a kind of flowchart where the arrows reflect the transfer of some kind of quantity from one thing to another. The size of the flow is indicated by the width of the arrows.

• When to Use: The flow of energy in a power system, or the distribution of water resources, are two examples of situations where a Sankey diagram may be helpful.

• Examples: The flow of money in a financial system or the breakdown of GHG emissions by industry might both be shown using a Sankey diagram.

A radar chart is a kind of grid chart in which many independent variables are shown on radial spokes emanating from a central point.

• When to Use: Radar charts are helpful for comparing the values of many distinct factors, such the effectiveness of various items or the proficiency of various personnel.

• Examples: A radar chart may be used to compare the success of several movies along many dimensions or to track the progress of a sports team across multiple metrics.

Comparison of Types of Data Visualization

1. Effectiveness in Displaying Different Types of Data

There are advantages and disadvantages to using each data visualisation method for showing various kinds of information:

• Bar graphs are useful for demonstrating trends and comparing discrete data.
• In addition to presenting continuous data, line graphs may be used to illustrate temporal trends and patterns.
• Pie charts may be used to show how various components of a whole relate to one another.
• Relationships and correlations between two variables may be visualised with the use of scatter plots.
• When analysing huge datasets, heat maps are helpful for visualising data distributions and discovering trends.
• When it comes to visualising hierarchical data and comparing the sizes of various categories, treemaps shine.
• Network diagrams are helpful because they may show the interconnections between various things.
• Chord diagrams may be used to clearly demonstrate processes and relationships between various entities.
• The flow and distribution of quantities between entities may be visualised using Sankey diagrams.
• The values of many different variables may be compared easily using radar plots.

2. Limitations and Drawbacks of Each Type

Each data visualisation method has advantages and disadvantages that must be weighed when deciding which is best for a given dataset.

• When the scale isn't right or there are too many bars, a bar graph might be difficult to interpret.
• The use of an improper scale or an excessive number of lines in a line graph may also lead to erroneous conclusions.
• While they may seem nice, pie charts don't do a good job of displaying trends over time.
• If there is a lot of background noise or the data is not strongly connected, the scatter plot may be hard to decipher.
• Too many data points or an arbitrary colour scheme may make heat maps unintelligible.
• If there are too many categories or the data is not well-structured hierarchically, the treemap may be difficult to understand.
• Too many nodes and edges in a network diagram might make it hard to follow the connections between them.
• When there are too many nodes and arcs in a chord diagram, it might be confusing to the reader.
• Too many flows and arrows may make a Sankey diagram hard to understand.
• When comparing radar charts, it might be tricky if the axes aren't uniformly spaced or there are too many factors to consider.

Conclusion

In conclusion, data visualisation is a powerful method of conveying data-driven findings. Different data visualisation methods have their own advantages and disadvantages. Treemaps, network diagrams, chord diagrams, Sankey diagrams, and radar charts are some of the more complex varieties, in addition to the more common bar graphs, line graphs, pies, scatter plots, and heat maps.

Choosing the right data visualisation method is critical for conveying insights from data. Data misinterpretation and misunderstanding are possible outcomes of using the incorrect visualisation method. The data being presented, the message being delivered, and the intended viewers are all important considerations when settling on a visualisation style. Data analysts may aid decision-makers in gaining insights and making educated choices by selecting the appropriate kind of visualisation and using it effectively.

Attending a data science course is a great way to gain knowledge in this area and get experience with data visualisation. Data visualisation is only one of the many data science tools that may be better understood by enrolling in a course. Opportunities in many fields may be found in data science training due to the growing need for data-driven decisions.