You’re looking to improve your data visualization skills. What are the most important techniques to learn?

1 Know your data

Before you start designing your data visualization, you need to understand your data. What are the main variables, dimensions, and measures? How are they related, distributed, and aggregated? What are the patterns, trends, and outliers? You can use various tools and methods to explore your data, such as summary statistics, histograms, scatter plots, and correlation matrices. By knowing your data, you can choose the most appropriate type of visualization, such as bar charts, line charts, pie charts, maps, or dashboards.

2 Know your audience

Another key factor to consider when creating your data visualization is your audience. Who are they, what do they know, and what do they want to learn from your data? You should tailor your data visualization to suit their needs, interests, and expectations. For example, if your audience is familiar with your data, you can use more complex or advanced visualizations, such as heat maps, network graphs, or interactive charts. If your audience is new to your data, you should use simpler or more intuitive visualizations, such as tables, histograms, or maps.

3 Know your message

The third technique to improve your data visualization skills is to know your message. What is the main point or takeaway that you want to convey with your data? You should have a clear and concise message that summarizes your findings, conclusions, or recommendations. You should also use visual elements, such as colors, shapes, sizes, labels, and annotations, to highlight your message and draw attention to the most important or relevant parts of your data. You should avoid cluttering your data visualization with unnecessary or distracting details that might confuse or mislead your audience.

4 Know your principles

It is important to know your principles when trying to improve your data visualization skills. These principles are general guidelines or best practices that can help you create effective and ethical data visualizations. For example, it is necessary to choose the right type of visualization for your data and message, as well as use consistent and appropriate scales, axes, and legends. Furthermore, colors and contrasts should be used to enhance readability and aesthetics, labels and titles should be used to provide context and clarity, charts and graphs should be used to show comparisons, relationships, and changes, tables and text should be used to show numbers, details, and explanations. Additionally, white space and alignment should be used to create balance and harmony. Finally, animation and interactivity can add functionality and engagement.

5 Know your tools

To improve your data visualization skills, it's important to know your tools. These tools are software or applications that can help you create, edit, and share data visualizations. Popular tools include Excel, a spreadsheet program for creating basic charts and graphs; PowerPoint, a presentation program for creating and displaying data visualizations; Tableau, a data visualization platform for creating interactive dashboards and stories; R, a programming language for creating customized and advanced data visualizations; and Python, another programming language that can utilize various libraries such as matplotlib, seaborn, or plotly to create data visualizations. These tools range from simple and free to complex and expensive.

6 Know your resources

Knowing your resources is the last technique to improve your data visualization skills. Resources can provide information, inspiration, and feedback to help you learn and improve. Books such as The Visual Display of Quantitative Information by Edward Tufte, Storytelling with Data by Cole Nussbaumer Knaflic, or Data Visualization: A Practical Introduction by Kieran Healy are useful for learning theory and practice. Blogs like FlowingData, Visualising Data, or The Pudding can show the latest trends and examples. Courses like Data Visualization and Communication with Tableau by Duke University, Data Visualization with R by Johns Hopkins University, or Python for Data Visualization by IBM can teach skills and tools. Communities like Reddit, Twitter, or LinkedIn help connect with other enthusiasts. Feedback can be obtained from colleagues, friends, family, or online platforms such as DataVizFeedback or MakeoverMonday.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?