5 Essential Data Visualization Skills

Written by Coursera Staff • Updated on

As data becomes increasingly important in today's economy, strong data visualization skills can boost your career prospects. Learn the essential skills, tools, and techniques needed to effectively present data and communicate insights.

[Featured Image] Data analyst presents in front of a projected screen with essential data visualizations in the background.

Key takeaways

  • Data visualization skills involve creating charts, dashboards, and infographics that transform complex datasets into clear, actionable insights for business audiences.

  • Strong data visualization skills can enhance your resume and boost your confidence as organizations increasingly need professionals who can communicate data findings effectively.

  • You can develop these skills by learning tools like Tableau or Power BI, practicing design principles, and building a portfolio with real-world data projects.

Learn the core data visualization skills, tools, and techniques needed to create effective charts and dashboards, plus strategies to develop these abilities in the following article. If you're ready to get started right away, you can develop key data visualization skills like data modeling, correlation analysis, and business intelligence through Microsoft's Data Visualization Professional Certificate program. By the end, you'll be able to create dashboards and interactive reports in Power BI.

5 Essential data visualization skills

While data analysis helps businesses discover customer insights and improve decision-making, you need strong visualization skills to communicate these findings effectively to stakeholders. This involves mastering design principles and tools to tell compelling stories with your data. If your job involves analyzing data, you should develop strong visualization skills to present your findings effectively. The following sections outline core competencies to master.

1. Knowledge of data visualization design principles

When creating data visualizations, follow key design principles like simplicity, clarity, repetition, and symmetry. Here's a closer look at these fundamentals:

  • Simplicity: Your goal is to present the insights from the data in a visual format that is easy for your audience to understand. To accomplish this, consider using only the information necessary to convey the meaning of the data. Use simple charts with minimal gridlines and labels while also refraining from incorporating too many decorative elements. 

  • Clarity: If you are comparing, for example, artificial intelligence usage across industries, you might simplify the visualization of the information by using a basic color scheme and a bar graph rather than a complex 3-D chart. 

  • Repetition: Present your findings using similar types of charts, colors, or shapes throughout a presentation. Repetition forms a connection between the elements of your presentation, which reminds your audience that a relationship exists within the data. 

  • Symmetry: The human brain is attracted to symmetry, so when your visual presentation contains this principle, it helps your audience better understand the information. 

Is data visualization a technical skill?

Yes, data visualization is considered a technical skill, though it combines both technical and creative elements. It requires proficiency with software tools, understanding of data formats, and often some coding knowledge, along with design principles and analytical thinking.

2. Familiarity with data visualization tools

Choosing the right visual aid to tell the story of your data is an important skill in the field of data visualization. When trying to determine the proper type of chart for presenting your information, consider if you are displaying the data as a comparison, a distribution, a composition, or a relationship.

  • Comparison: Consider using a line graph or a bar graph.

  • Distribution: If you have many data points, a histogram may be a good choice. If you only have two or three variables to show, a chart might be more effective.

  • Composition: A column or an area chart may be effective in this case because you can show changes over time.

  • Relationship: When trying to show a relationship within the data, a scatter or bubble plot tends to work well.

Data visualization tools

To create effective data visualizations, you'll need proficiency in tools like Tableau, Power BI, and Adobe Creative Suite. These platforms enable you to build modern, professional visual displays quickly and efficiently.

Read more: The Best Data Visualization Tools for Businesses

3. Programming proficiency

To expand your skill set in this discipline, you can learn programming languages, such as R and Python. These programming languages can assist you in converting big data into helpful graphics. You can use R to create scatter plots, box plots, and histograms of your data. People working in data science tend to use Python frequently, and in terms of data visualization, it offers several graphing libraries such as pandas visualization, Plotly, and seaborn. 

Read more: Python or R for Data Analysis: Which Should You Learn?

Familiarity with data manipulation libraries

Another data visualization skill you want to develop is an understanding of data manipulation libraries, which are tools for processing, cleaning, and analyzing data. A few of the most effective Python data manipulation libraries are pandas, NumPy, Dask, Polars, and PySpark. Using these libraries can make it easier for you to manipulate, analyze, and visualize your data.

Read more: What Are Python Libraries for Data Science?

4. Data storytelling

To transform raw data into valuable insights, identify the story within your data and use visualization tools to communicate it effectively. Strong data storytelling helps organizations make better decisions and drive growth. Here are the key components:

Crafting narratives with visualizations

Typically, the foundation of your data’s story is a combination of three elements: data, narrative, and visualization. First, your data, which is the bedrock of the story, needs to be collected from reliable sources and thoroughly analyzed. Next, build your narrative, which is the detailed part of your story that explains the important findings within the data. The narrative functions as the binding element of a data’s story because it is the portion in which you reveal the connections within the data.

Use charts, graphs, and diagrams to visually convey your narrative, making the data’s story and its insights more accessible to your audience. Hopefully, these new insights will lead to improved decision-making. 

Presenting insights effectively

You can effectively present the insights from the data by following a few simple tips: Organize the data, know the goal of your story, create an outline for it, and identify the actions you would like your audience to take after hearing the story.

5. Analytical skills

A well-developed set of analytical skills can also help you to gather insights from the data before successfully presenting it visually. When working with data, you want to be able to identify patterns, understand data distributions, and conduct statistical analyses. Developing your analytical skills means you’re more likely to create accurate and reliable data visualizations, which allow for better storytelling.

Identifying key trends and patterns

Understanding how to work with data in order to identify patterns will likely make it easier for you to construct your visualizations. Make sure you have a solid grasp of the source data so you can spot trends in the information. These patterns and trends in the data are what you will use to build your visualizations.

Understanding data context

For your audience to understand the meaning behind your data, make sure to provide context for it. When telling the data’s story to your audience, provide a clear connection between the data points so viewers can more easily comprehend what the information means. This can help them when it comes time to make decisions. Providing context can be the difference between telling a superficial story with the data and telling a story that moves people to take action.

How to improve data visualization skills

Building your data visualization skills requires a combination of passion for numbers, as well as a desire to transform the right data into stunning visualizations. 

1. Learn data visualization tools and software.

Being able to navigate and use tools such as Excel and other data visualization software is critical to success in your data visualization journey. Understanding how to use at least one or two software platforms (like Tableau) is beneficial because you can easily translate those skills from one platform to another.

Tools such as Tableau, Microsoft Power BI, Plotly, Sisense, and Qlikview allow you to create graphs, charts, and dashboards for organizations. You’ll want to be able to create several different types of data visualizations, to create outputs for reports, presentations, and more. Some techniques include infographics, scatter plots, fever charts, and population pyramids.

Read more: 5 Business Intelligence Tools You Need to Know

2. Practice working with data and analyzing it.

Beyond using the BI tools, you’ll want to be an expert in wading through structured and unstructured data from several sources to make sense of it all. Not only should you be able to collect, sort, and clean up the data, but you should also be able to analyze it from multiple perspectives to generate efficient, logical, and actionable outcomes.

You’ll need to know the five categories of data visualization (hierarchical, geospatial, multidimensional, temporal, and network) and be comfortable producing charts for each. Within these categories, you might work with tree diagrams, density maps, Venn Diagrams, scatter plots, and word clouds.

3. Develop excellent storytelling and communication skills.

Some data-oriented roles focus only on sorting and managing data. But for any job that requires data visualization, the role goes beyond producing beautiful charts and graphs. You’ll also be expected to explain what data sets were used, your analysis, why you used this visualization, and your strategic recommendations. Being able to engage your audiences with visual and verbal storytelling is a skill that can take you far in data visualization jobs.

In your role, you may work with stakeholders such as product managers, data engineers, data analysts, and IT support. Together, you’ll collaborate to create high-performing, scalable solutions. To effectively work as a team, you’ll want to speak their respective languages, using the correct terminology and conceptual ideas to ensure your story and recommendations have authority.

Putting your skills into practice

Members, in places such as the Tableau Community, develop and host challenges where you can use and practice your data visualization skills. You can apply your knowledge in this field to various topics and products. If you’re relatively new to data visualization, the Tableau Community offers projects for beginners that help you establish the foundational skills for this field. This community also provides projects focused on how to tell stories with data.

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