Data visualization is a way to represent data in a graphical or pictorial format. It is an important tool for understanding and communicating data, as it helps to make complex data more accessible and easier to understand. There are several fundamental principles to consider when designing a data visualization, including:
There are many different types of charts and graphs that can be used to visualize data, including bar charts, line charts, scatter plots, and pie charts. It is important to choose the appropriate chart type for the data you are visualizing and the message you want to convey. We'll cover this some other day :)
In addition to these basic chart types, there are many other specialized charts and graphs that can be used to visualize specific types of data or to show more complex relationships. Some examples include maps, network diagrams, and tree diagrams etc.
Best practices for creating effective visualizations
Apart from the common fundamental principles that we have listed above, there are some best practices for creating effective visualizations: I have listed a few below as follows:
Clearly define the purpose of the visualization: It is important to have a clear understanding of the message or information you want to communicate through the visualization. This will help you choose the appropriate chart type and design elements.
Use appropriate chart types: Choose the chart type that is most suitable for the data you are visualizing and the message you want to convey. For example, a bar chart is a good choice for comparing the values of different categories, while a line chart is well suited for showing trends over time.
Use effective visualization techniques: Use design elements such as color, layout, and typography effectively to enhance the impact of the visualization. Avoid using too many colors or using colors that are hard to distinguish from one another. Use a clear and readable font, and consider using labels and annotations to help the viewer understand the visualization.
Keep it simple: Avoid cluttering the visualization with unnecessary elements or information. Use only the data that is necessary to convey the message or information, and remove any elements that distract from the main message.
Test and iterate: Show the visualization to others and get their feedback. Consider making changes based on their feedback and testing the revised visualization again. This can help to ensure that the visualization is effective and easy to understand.
Tools for creating visualizations
There are many tools available for creating visualizations, which are graphical representations of data that can help to convey information in a clear and concise manner. These tools include spreadsheet programs like Excel, specialized business intelligence platforms like Tableau, and programming libraries such as ggplot and D3.js. These tools offer a range of options for creating various types of charts and graphs, including line charts, bar charts, scatter plots, and maps. They also often provide customization options and the ability to create interactive visualizations. By using these tools, individuals and organizations can effectively communicate data and insights to a wide range of audiences.
Excel: Microsoft Excel is a spreadsheet program that includes many built-in tools for creating charts and graphs. It is commonly used for creating basic charts and graphs, such as line charts, bar charts, and pie charts.
Tableau: Tableau is a data visualization and business intelligence platform that allows users to create interactive dashboards and charts. It includes a wide range of visualization types, including maps, scatter plots, and histograms.
ggplot: ggplot is a data visualization library for the programming language R. It is particularly well-suited for creating high-quality plots and charts, and has a large user community that has developed a wide range of extensions and add-ons.
matplotlib: matplotlib is a data visualization library for the programming language Python. It is a powerful tool for creating a wide range of static and interactive plots and charts, and is widely used in the scientific Python community.
Plotly: Plotly is a data visualization platform that allows users to create a wide range of static and interactive plots and charts. It includes a range of customization options and is available in both online and offline versions.
Google Charts: Google Charts is a free, web-based data visualization platform that allows users to create a wide range of charts and graphs. It is easy to use and requires no programming knowledge.
RAWGraphs: RAWGraphs is an open-source tool for creating custom data visualizations. It is particularly well-suited for users who want to create complex visualizations quickly, as it includes a range of pre-built templates and customization options.
Among many other visualization tools, I will try practically come up with tools (in use-cases) that will be able to bring out how we can clearly use this visualization tools and what data is needed for each one. That's like in 2023 :)