How Difficult is it to Learn Data Visualization?

Are you curious about learning data visualization but worried it might be too hard? Of course, the difficulty that comes with learning a new skill is somewhat subjective. The challenges of learning data visualization depend on whether you have a background in data analytics, if you know basic design concepts, and how familiar you are with programs such as Microsoft Excel and Tableau. No matter your current schedule or comfort level with data visualization, plenty of tools are available to help make learning more manageable than you might think.

What is Data Visualization?

Data visualization uses graphics like plots, charts, or animations to represent data findings. These visual depictions communicate complicated data relationships in an accessible and engaging format and have a range of applications for sharing data-driven insights with an organization’s internal and external stakeholders. Because an estimated 65% of the population comprises visual learners, data visualization provides a way to quickly and effectively convey data findings. Even though data visualization takes many forms, it shares the common goal of communicating otherwise complex data insights in a memorable and understandable way, even for those who don’t come from a technical background.

Each day, 2.5 quintillion bytes of data are being created, which continues to grow exponentially. This increase in the amount of data has created a corresponding need for visualization practices to share the insights these numbers contain. Data visualization has a variety of uses across industries and professions. It has applications for describing trends or patterns hidden in data and communicating organizational structures or hierarchies. The data visualization process is typically broken into four purposes: to generate ideas, to illustrate concepts, to provide avenues into visual discovery, and to convey data in the form of visualizations.

Read more about what data visualization is and why you should learn it.

What Can You Do with Data Visualization?

Those with the skills to create engaging and effective data visualizations have the power to communicate detailed data insights and findings to audience members from all backgrounds. Data visualizations provide a concise and interactive way to depict data trends. Visualizations incorporate color and contrast to present information like sales patterns so that audience members can quickly observe information that may be difficult to see from a graph or spreadsheet. In addition, another benefit of working with data visualizations is that they can provide perspective on the data by placing it in the context of a larger story, which can help audience members understand the information as it relates to an organization’s overall goals or bottom line. Context plays an integral role in helping audiences understand the entirety of the data being depicted, which would be difficult to convey by listing numbers in a table.

Data visualization also provides a better understanding of a business’s operations. Running a business requires staying on top of many moving parts, and data visualizations provide a tool that can depict multifaceted operations and how different business activities connect. Moreover, analyzing and visualizing data provides insights into important business metrics that can lead to smoother daily operations and more successful project execution. For organizations that sell products or services to customers, data visualizations offer crucial insights into customers’ preferences, demographics, and shopping patterns. They provide a way for organizations to use customer-focused data to explore brand perception, customer sentiments, and even what customers post about the product or service on social media outlets. Businesses can then use these insights to tailor marketing strategies and brand outreach.

In addition to providing customer insights and depicting important trends in the data, visualizations also tell a story. Information is presented in an easy-to-understand format, and the audience is taken on a journey that leads to an inevitable, data-driven conclusion. Just like a written story, a visual story has a strong beginning, a basic plot, and an ending that looks into the future, providing recommendations for how to move forward. Instead of requiring tens of thousands of words to tell this story, data visualizations can do so with a series of well-placed infographics, carefully worded labels, and brief descriptions of the content, all of which tell the story of data in the larger context of an organization’s needs and goals. 

What Are the Most Challenging Parts of Learning Data Visualization?

Some consider the following to be the most challenging aspects of learning how to visualize data:

• Deciding what data to include in a visualization. The process of sorting and ultimately deciding what to include in a data visualization can be complicated for some people. Every choice you make when collecting and organizing data has a bias, even if it’s not intentional. It requires you to be aware of your perspective, the perspective of the data, and the perspective of the audience viewing the visualization. For example, deciding to include economic, racial, or gender-specific information should be deliberately considered to avoid misconceptions.

• Including too much data in a visualization. One of the most common design problems in any graphical representation is including too much information. While it’s essential to have clearly labeled variables and text descriptions, you don’t want to overwhelm the viewer with too much information, or else they may miss the takeaway you hope to provide. Visualizations that provide too much data can lead to a graphic soup that audience members won’t be able to understand. Suppose you struggle with whether your data visualization includes too much data. In that case, you may consider breaking it into two visualizations, each of which can more effectively communicate one portion of the information.

• Selecting the wrong visualization method. The decision of which graphical representation you wish to use for data visualization is crucial. Whereas pie charts convey a relationship of parts to a whole (such as budget breakdowns), they may not work for other tasks, such as comparing different datasets. Because different data visualization methods aren’t a one-size-fits-all tool, it’s essential to know the variables you’re looking to communicate, then select the method most suited to the data.

• Misusing color contrast. Color is a powerful tool in data visualization. It’s one of the most essential design elements and can make or break a data visualization. Even using subtle shade variations can cause strong responses from audience members. Whereas it may seem like a good idea to use high degrees of color contrast in your visualization, doing so can be problematic because it can convey to viewers that value disparities are more significant than they are. Although you can use color quite effectively to highlight important data findings and differentiate between different data series, it’s important to be mindful of how you use color. Most experts suggest avoiding high-contrast pairings that may skew viewers’ understanding of the data findings.

How Does Learning Data Visualization Compare to Other Fields?

The field of data visualization is closely related to data analytics and even overlaps with it in some ways. Generally, data visualization pertains to graphically representing data and information using maps, charts, graphs, and other visual formats. Data analytics is more involved with analyzing large datasets to extract meaningful information. In some ways, data analysis can be considered the first step in the data analytics process, with data visualization playing an important role post-data analysis. Whereas data analytics aims to collect and analyze data that can help an organization make more informed business decisions, data visualization strives to communicate data findings in a clear, easy-to-understand, and accessible way to a range of audience members. 

Those working in data analytics may seek training in programs such as Microsoft Excel and programming languages like Python, SQL, or R. These tools are essential for quickly organizing and analyzing data. On the other hand, individuals who focus more on visualizing data may still have training with Excel and various programming languages but also work with Tableau, Datawrapper, Infogram, or Google Charts to convey data analysis results visually. Data Analysts often perform data visualization as part of their job, meaning that these two skill sets can overlap and inform one another in various professional settings. For Data Analysts tasked with communicating data findings in Tableau or another application, additional training is sometimes required to master the visualization tool. 

The Importance of Good Data Visualization

Creating engaging data visualizations is one of the most valuable skills in today’s job market. Whether these visualizations are designed using Python, Excel, or Power BI’s dashboarding capabilities, more companies than ever are considering themselves “data-driven” and seeking individuals to convey data graphically. Not only is visualizing data an in-demand skill, but so is understanding visualizations and correctly interpreting data.

Although visualization methods such as graphs, charts, and plots may not seem that important in themselves, when used for business ventures, well-executed visualizations can mean the difference between success and disaster. For hundreds, if not thousands of years, those with a business mindset have relied on visualized data as an essential tool for analysis and have used it for predictive and prescriptive endeavors. Still today, data visualization is a powerful tool for helping organizations anticipate what’s to come and interpret what has already transpired. Those with the skills to visualize data accurately have the power to inform audience members, contribute to an organization’s decision-making process, and affect real change. 

Which Companies use Data Visualization?

The list of organizations that use data visualization is extensive. This tool has grown in popularity and is shared across industries and professions. Here are just a few companies that rely on data visualizations to convey crucial information to decision-makers and a brief description of how they do so:

• Johns Hopkins University has been using visual storytelling to gather the most current COVID-19 information to create a dashboard that depicts real-time health-related updates accessible to a non-technical audience.
• Google uses data visualizations for its annual Year in Search campaign, which showcases the most widely searched terms from the prior year. 
• National Geographic created The Atlas of Moons using data visualizations. This interactive online guide allows viewers to move through space and explore different moons.
• Whirlpool’s Care Counts campaign was launched in 2016 to ensure public schools have washers and dryers. Their data showed that 4,000 kids had been dropping out of school a year due to not having clean clothes. Data visualization helped them showcase their brand’s values.
• Spotify uses data visualization to design a yearly “Wrapped” slideshow that depicts users’ listening history. This slideshow features listeners’ favorite music genre, singer, or song. It also gathers data for ad placement between songs.

Learn Data Visualization with Hands-on Training at Noble Desktop

If you’re interested in studying data visualization, Noble Desktop offers several in-person and live online courses that provide hands-on training. For those who wish to work with Tableau for visualizing data, Noble’s Tableau for Data Visualization Bootcamp is an excellent option. Participants receive an overview of the field of data visualization and learn about Tableau Public’s visualization tools. By course completion, students will have a solid understanding of identifying which datasets to connect to and how to analyze, filter, and organize data to make customized, publishable visualizations. This course also offers the option of a free retake for those interested in revisiting Tableau concepts. 

Noble’s in-person or live online Data Analytics Certificate is another good learning option for those seeking a more intensive study option. This rigorous program provides expert instruction on various data analytics concepts and prepares learners to become Business Analysts or Data Analysts. By course completion, all participants will be familiar with core business intelligence, statistical analysis, data analysis, and data visualization concepts. This is a project-based course in which students will complete various real-world projects using prescriptive and predictive analytics. All students receive one-on-one mentoring as part of tuition.

In addition, other in-person and live online data visualization courses are also available from Noble Desktop. Those who wish to learn the tools to become Data Scientists may consider enrolling in Noble’s Data Science Certificate, which covers machine learning, automation, SQL, and Python. A Python for Data Science Bootcamp covers fundamental and complex Python programming concepts, such as creating programs and using statistics to make machine learning models. 

The Excel Bootcamp is also available, which teaches core spreadsheet functions like working with macros, formulas, and PivotTables. Finally, those interested in working with SQL to extract information from databases can enroll in Noble’s SQL Bootcamp. This course prepares learners to write queries, aggregate data, and filter results using Postgre SQL.