Getting started with data visualization + types and tools

Hey, take a look at this!  

Pie charts with multicolored wedges. Graphs showing record profits–or losses–via zigzagging lines. Interactive maps that show you county-by-county health statistics in a mosaic of blues, reds, and yellows. These are just some of the ways that data can be visualized to improve accessibility, heighten understanding, and encourage others to take action.

In previous issues, we’ve discussed the power of storytelling, graphic design, and data analytics. Data visualization, or the process of crafting eye-catching visuals to illustrate data insights, occurs when you combine all of these techniques. It’s a seriously impressive process that can yield truly impactful, informative, and important results in organizations of all sizes. 

Today, we’re exploring data visualizations, common tools used to create them, and some common types of visuals that can help your data insights shine. Let’s get started. 

But first, do you want to learn data visualization and analytics skills? Enroll in the Microsoft Data Visualization Professional Certificate to learn how to analyze data and craft compelling visualizations that drive strategic decision-making. 

📊What is data visualization? 

Data visualization is the representation of information and data using charts, graphs, maps, and other visual tools. While data analysis can reveal a wide range of important insights, data visualization helps to clearly illustrate those insights to others, such as organizational leaders, special interest groups, the general public, and other relevant stakeholders.  

Data visualizations help drive informed decision-making by adding color, graphics, and form to otherwise bland, abstract, and shapeless data. There are many benefits to visualizing data. Some common uses include:

• Storytelling: Colors, patterns, and diagrams can help to visualize the story you’re trying to tell with data. 
• Accessibility: Visualizations make information easier to understand for a variety of audiences. 
• Exploration: More accessible data means more opportunity to explore, collaborate, and inform actionable decisions. 
• Identifying relationships: Data visualization makes it easier to spot relationships and patterns within data. 

💻Data visualization tools 

There are many kinds of data visualization tools available to suit your needs. Some common ones include Excel, Google Sheets, Google Charts, Tableau, Power BI, Jupyter, Infogram, and Grafana. Before you commit to one of these tools, though, make sure to thoroughly research them to see if they best fit your desired goals, outcomes, and needs. 

📈Types of data visuals 

Data visualizations can range from simple bar and pie charts to complex, interactive websites that allow visitors to explore data themselves. There’s a lot for you to work with. Some common types of data visuals include: 

• Tables: Data is displayed in rows and columns, making it easily navigated and accessible to others.

• Graphs: Data is plotted on an X and Y axis, usually with bars, points, or lines, for comparison purposes. 

• Gannt charts: A kind of bar chart used in project management to display timelines and tasks. 

• Geospatial visualizations: Data is depicted on a map with shapes and colors to illustrate a trend within a specific region. 

📚How to learn data visualization

Don’t let your insights get lost in a sea of uninspired spreadsheets. Build core data visualization and analytics skills with these programs on Coursera: 

For data analysis and visualization skills, enroll in the Microsoft Data Visualization Professional Certificate. Learn how to analyze and transform data into actionable insights and compelling visualizations that drive strategic business decisions. 

To learn Tableau for data visualization, try Tableau’s own Data Visualization with Tableau course. Plus, upon completion, you’ll be one step closer to completing Tableau’s Business Intelligence Analyst Professional Certificate. 

To use data to tell a story, explore DeepLearning.AI’s Data Storytelling course. Afterward, if you want to develop robust data analytics skills, consider completing the full DeepLearning.AI Data Analytics Professional Certificate. 

For visualizing data with R, look into Johns Hopkins’ Data Visualization & Dashboarding with R Specialization. Learn to use R to create static and interactive data visualizations and publish them on the web to reach many types of audiences.

Okay, that’s all we’ve got for this week! Before you break out the Pantone color chips to decide on the perfect hue for your next pie chart masterpiece, consider letting us know your favorite data visualization technique in the comments below. Later!

Have a career question you’d like us to answer next? Share it below. And, as always, we welcome YOUR advice and experience in the comments.