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Data Visualization: Definition, Benefits, and Examples

Data visualization helps data professionals tell a story with data. Here’s a definitive guide to data visualization.

Data visualization is a powerful way for people, especially data professionals, to display data so that it can be interpreted easily. It helps tell a story with data, by turning spreadsheets of numbers into stunning graphs and charts.

In this article, you’ll learn all about data visualization, including its definition, benefits, examples, types, and tools. If you decide you want to learn the skills to incorporate it into your job, we'll point you toward online courses you can do from anywhere.

What is data visualization?

Data visualization is the representation of information and data using charts, graphs, maps, and other visual tools. These visualizations allow us to easily understand any patterns, trends, or outliers in a data set.

Data visualization also presents data to the general public or specific audiences without technical knowledge in an accessible manner. For example, the health agency in a government might provide a map of vaccinated regions.

The purpose of data visualization is to help drive informed decision-making and to add colorful meaning to an otherwise bland database.

Benefits of data visualization

Data visualization can be used in many contexts in nearly every field, like public policy, finance, marketing, retail, education, sports, history, and more. Here are the benefits of data visualization:

Storytelling: People are drawn to colors and patterns in clothing, arts and culture, architecture, and more. Data is no different—colors and patterns allow us to visualize the story within the data.

Accessibility: Information is shared in an accessible, easy-to-understand manner for a variety of audiences.

Visualize relationships: It’s easier to spot the relationships and patterns within a data set when the information is presented in a graph or chart.

Exploration: More accessible data means more opportunities to explore, collaborate, and inform actionable decisions.

Data visualization and big data

Companies collect “ big data ” and synthesize it into information. Data visualization helps portray significant insights—like a heat map to illustrate regions where individuals search for mental health assistance. To synthesize all that data, visualization software can be used in conjunction with data collecting software.

Tools for visualizing data

There are plenty of data visualization tools out there to suit your needs. Before committing to one, consider researching whether you need an open-source site or could simply create a graph using Excel or Google Charts. The following are common data visualization tools that could suit your needs. 

Google Charts

ChartBlocks

FusionCharts

Get started with a free tool

No matter the field, using visual representations to illustrate data can be immensely powerful. Tableau has a free public tool that anyone can use to create stunning visualizations for a school project, non-profit, or small business. 

Types of data visualization

Visualizing data can be as simple as a bar graph or scatter plot but becomes powerful when analyzing, for example, the median age of the United States Congress vis-a-vis the median age of Americans . Here are some common types of data visualizations:

Table: A table is data displayed in rows and columns, which can be easily created in a Word document or Excel spreadsheet.

Chart or graph: Information is presented in tabular form with data displayed along an x and y axis, usually with bars, points, or lines, to represent data in comparison. An infographic is a special type of chart that combines visuals and words to illustrate the data.

Gantt chart: A Gantt chart is a bar chart that portrays a timeline and tasks specifically used in project management.

Pie chart: A pie chart divides data into percentages featured in “slices” of a pie, all adding up to 100%. 

Geospatial visualization: Data is depicted in map form with shapes and colors that illustrate the relationship between specific locations, such as a choropleth or heat map.

Dashboard: Data and visualizations are displayed, usually for business purposes, to help analysts understand and present data.

Data visualization examples

Using data visualization tools, different types of charts and graphs can be created to illustrate important data. These are a few examples of data visualization in the real world:

Data science: Data scientists and researchers have access to libraries using programming languages or tools such as Python or R, which they use to understand and identify patterns in data sets. Tools help these data professionals work more efficiently by coding research with colors, plots, lines, and shapes.

Marketing: Tracking data such as web traffic and social media analytics can help marketers analyze how customers find their products and whether they are early adopters or more of a laggard buyer. Charts and graphs can synthesize data for marketers and stakeholders to better understand these trends. 

Finance: Investors and advisors focused on buying and selling stocks, bonds, dividends, and other commodities will analyze the movement of prices over time to determine which are worth purchasing for short- or long-term periods. Line graphs help financial analysts visualize this data, toggling between months, years, and even decades.

Health policy: Policymakers can use choropleth maps, which are divided by geographical area (nations, states, continents) by colors. They can, for example, use these maps to demonstrate the mortality rates of cancer or ebola in different parts of the world.  

Tackle big business decisions by backing them up with data analytics. Google's Data Analytics Professional Certificate can boost your skills:

Jobs that use data visualization

From marketing to data analytics, data visualization is a skill that can be beneficial to many industries. Building your skills in data visualization can help in the following jobs:

Data visualization analyst: As a data visualization analyst (or specialist), you’d be responsible for creating and editing visual content such as maps, charts, and infographics from large data sets. 

Data visualization engineer: Data visualization engineers and developers are experts in both maneuvering data with SQL, as well as assisting product teams in creating user-friendly dashboards that enable storytelling.

Data analyst: A data analyst collects, cleans, and interprets data sets to answer questions or solve business problems.

Data is everywhere. In creative roles such as graphic designer , content strategist, or social media specialist, data visualization expertise can help you solve challenging problems. You could create dashboards to track analytics as an email marketer or make infographics as a communications designer.

On the flip side, data professionals can benefit from data visualization skills to tell more impactful stories through data.

Read more: 5 Data Visualization Jobs (+ Ways to Build Your Skills Now)

Dive into data visualization

Learn the basics of data visualization with the University of California Davis’ Data Visualization with Tableau Specialization . You’ll leverage Tableau’s library of resources to learn best practices for data visualization and storytelling, learning from real-world and journalistic examples. Tableau is one of the most respected and accessible data visualization tools. 

To learn more about data visualization using Excel and Cognos Analytics, take a look at IBM’s Data Analysis and Visualization Foundations Specialization .

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6 Data Visualization Examples To Inspire Your Own

• 12 Jan 2017

Data informs virtually every business decision an organization makes. Because of this, it’s become increasingly important for professionals of all backgrounds to be adept at working with data.

While data can provide immense value, it’s important that professionals are able to effectively communicate the significance of the data to stakeholders. This is where data visualization comes into play. By transforming raw data into engaging visuals using various data visualization tools , it’s much easier to communicate insights gleaned from it.

Here are six real-world examples of data visualization that you can use to inspire your own.

What Is Data Visualization?

Data visualization is the process of turning raw data into graphical representations.

Visualizations make it easy to communicate trends in data and draw conclusions. When presented with a graph or chart, stakeholders can easily visualize the story the data is telling, rather than try to glean insights from raw data.

There are countless data visualization techniques , including:

• Scatter plots

The technique you use will vary based on the type of data you’re handling and what you’re trying to communicate.

6 Real-World Data Visualization Examples

1. the most common jobs by state.

Source: NPR

National Public Radio (NPR) produced a color-coded, interactive display of the most common jobs in each state in each year from 1978 to 2014. By dragging the scroll bar at the bottom of the map, you’re able to visualize occupational changes over time.

If you’re trying to represent geographical data, a map is the best way to go.

2. COVID-19 Hospitalization Rates

Source: CDC

Throughout the COVID-19 pandemic, the Centers for Disease Control and Prevention (CDC) has been transforming raw data into easily digestible visuals. This line graph represents COVID-19 hospitalization rates from March through November 2020.

The CDC tactfully incorporated color to place further emphasis on the stark increase in hospitalization rates, using a darker shade for lower values and a lighter shade for higher values.

3. Forecasted Revenue of Amazon.com

Source: Statista

Data visualizations aren’t limited to historical data. This bar chart created by Statista visualizes the forecasted gross revenue of Amazon.com from 2018 to 2025.

This visualization uses a creative title to summarize the main message that the data is conveying, as well as a darker orange color to spike out the most important data point.

4. Web-Related Statistics

Source: Internet Live Stats

Internet Live Stats has tracked web-related statistics and pioneered methods for visualizing data to show how different digital properties have ebbed and flowed over time.

Simple infographics like this one are particularly effective when your goal is to communicate key statistics rather than visualizing trends or forecasts.

5. Most Popular Food Delivery Items

Source: Eater

Eater, Vox’s food and dining brand, has created this fun take on a “pie” chart, which shows the most common foods ordered for delivery in each of the United States.

To visualize this data, Eater used a specific type of pie chart known as a spie chart. Spie charts are essentially pie charts in which you can vary the height of each segment to further visualize differences in data.

6. Netflix Viewing Patterns

Source: Vox

Vox created this interesting visualization depicting the viewing patterns of Netflix users over time by device type. This Sankey diagram visualizes the tendency of users to switch to streaming via larger device types.

Visualizing Data to Make Business Decisions

The insights and conclusions drawn from data visualizations can guide the decision-making and strategic planning processes for your organization.

To ensure your visualizations are relevant, accurate, and ethical, familiarize yourself with basic data science concepts . With a foundational knowledge in data science, you can maintain confidence in your data and better understand its significance. An online analytics course can help you get started.

Are you interested in improving your data science and analytical skills? Download our Beginner’s Guide to Data & Analytics to learn how you can leverage the power of data for professional and organizational success.

This post was updated on February 26, 2021. It was originally published on January 12, 2017.

How to Visualize Data: 6 Rules, Tips and Best Practices

Table of contents

Enjoy reading this blog post written by our experts or partners.

If you want to see what Databox can do for you, click here .

Creating a great visual report is a lot like being reporting on a news story. Let me explain. 

• You start by collecting all of your sources (i.e. data). 
• Next, you analyze all of the information/data. 
• Then, you put it altogether in a compelling story.

Great visual reporting is all about telling a compelling story through a mix of data visualizations. 

In this post, we’re taking a closer looking at what data visualization is along with some best practices.

What is Data Visualization?

Let’s dive in.  

Data visualization is the process of turning data into a compelling visual story through the use of graphics, like charts and graphs.  

It is one of the most effective ways to show trends and patterns from data analysis. 

So, it is no surprise that when we asked 57 data analysts how important data visualization is that 83% said it was very important.

And, 72.2% even confessed to saying that data visualization played a pivotal role in a project’s success. 

The two most common formats for visualizing data are dashboards and reports. This allows you to showcase several different images to paint a more compelling story. 

In fact, the average dashboard, according to our experts, contains 3-5 charts or graphs. When you use multiple charts in a dashboard, it is important to mix up the format. 

“If you use the same type of visualization every time you present data, it’s as boring as using the same structure in every sentence you speak,” says Melanie Musson of AutoInsureSavings.org . “You’ll lose your audience even if you have great information. Use all the charts but think outside the box. One of my favorite techniques to really make an impact is to use analogy. 

For example, if we had a significant improvement toward a goal, I might include a picture of a mouse indicating our small success last year, and an elephant to represent this year. It’s a little silly, but it mixes things up.”

This also doesn’t mean you need to stick to the most popular chart formats – line and bar graphs.

In fact, Eden Cheng of PeopleFinderFree says, “My personal favorite is the bubble chart. It looks attractive and displays information in a very impressive way. Here, the weight of the value is defined by the bubble circumference. So, one can easily spot which factor is important and which is not. 

It’s more of storytelling and conveys the idea impactfully. I use it to demonstrate data related to cost and value comparison, highlight the area of constant focus, and streamline activities. 

If a bubble chart is not used then my next pick is a customary pie chart. Without making things look clumsy, it conveys the information well. Both these data visualization ways are visually pleasant as multiple colors can be used and information can be conveyed without any mess. But, a pie chart can only be used if you have a few variables or factors to display.”

Angus Chang of Lupilon adds, “We (also) visualize our data with pie charts because it clearly shows proportion and is easy to understand. Pie charts show what percentage each value contributes to the total. The charts are more intuitive than simply listing percentage values that add up to 100%. Using this chart, we illustrate which campaign brings in the biggest share of total leads. A pie chart is our priority because it depicts data accurately and represents percentages.”

Others prefer using funnel charts. 

“I personally enjoy funnels and I think they are super helpful for understanding website analytics and just the general user journey as they’re interacting throughout your website,” says Elizabeth Weatherby of Wolf Consulting, LLC, “Funnels can show what page paths users chose, what exit pages users left the site on, and can even give you some solid insight into conversions. No matter what type of visualization you choose, just make sure your date ranges are accurate so you know exactly what data you’re viewing and what you’re comparing it to.”

It is also worth noting that charts and graphs aren’t the only type of visualization. You can also use word clouds to demonstrate trends.

“I use data visualization routinely in the form of word clouds,” explains Janice Wald of Mostly Blogging. “By posting the URL of my published content into a word cloud generator, I show the important results of my data collection since these appear in the biggest letter in the visual. I have just started incorporating line graphs into my content and found I rank higher and quicker. Google likes visuals, readers like visuals. Most people learn by looking at visuals. Incorporating visual data representation is a win-win.”

Pro Tip: Here Is Your Go-To Dashboard For Doing a Deeper Dive on Your Website Traffic and Conversion Sources

Like most marketers, you’re probably interested in how much traffic comes to your website and how much of that traffic converts into new leads and customers. But digital marketers want to know more, right? Where is that traffic coming from, and how much of it comes from specific channels, like organic search, social media, and referrers? Which social media and referral sites? Now you can do that deeper dive from one dashboard connected to your Google Analytics 4 account, including key metrics like:

• Overall sessions and users. A running daily or weekly total of website users and sessions during a certain period of time. 
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• Sessions vs duration . Compare the average amount of time users spend on your website to the number of sessions.
• Top channels . Which channels are the most effective at driving traffic to your website?
• Conversion rate . How effective is your website at converting visitors to leads or signups?
• Percentage of new sessions. How much of your website traffic represents new visitors?
• Social media site breakdown. Which social media sites drive the most traffic during a certain period of time and compared to the previous period? How does that compare to organic search traffic?
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Now you can benefit from the experience of our Google Analytics 4 experts, who have put together a plug-and-play Databox template that contains all the essential metrics for monitoring and analyzing your website traffic and its sources, social media networks and referral sites and pages. It’s simple to implement and start using as a standalone dashboard or in marketing reports, and best of all, it’s free!

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Here are some of the most important data visualization rules according to the data experts that we surveyed. 

• Keep it simple
• Add white space
• Use purposeful design principles
• Focus on these three elements
• Make it easy to compare data
• Blend your data sources

1. Keep it simple

There is a tendency to overcomplicate it and add all of the charts, graphs, and data into your

dashboards and reports. This makes it hard to see what’s going on unless you are using dedicated marketing reporting software .  Being concise and telling your story through the fewest, but most impactful charts are almost always the way to go.  

“The main reason to visualize data is to get a point across,” says Amber Theurer of ivee. “For example, if your sales have increased within the past month, it would make sense for there to be a graph showing an incline of sales going in an upward motion from the left to the right side of the screen.

You don’t need to show any extra data that does not pertain to this topic, unless that data is covered on another page within another topic. Especially if you are presenting this data to your teammates at work, you don’t want to bore people with every single minute detail from your data to the point where they might lose interest.

Keep each data visualization clear and simple so that others will be able to easily understand what the data conveys.”

2. Add white space  

A good rule of thumb is when in doubt, add more white space to your data dashboard . 

“Well, I believe that the No. 1 rule for good data visualization is to let your data breathe,” says David Wurst of Webcitz. “When it comes to data visualization, one of the most common mistakes people make is trying to cram too much visual information into a single design. Whether it’s to “spice up” a design or to convey too much data, the end result is always the same: a visually cluttered, unintelligible design that asks more questions than it answers.”

Will Ward of Translation Equipment HQ adds, “Our number one rule with data visualization is to avoid ‘chartjunk’. This refers to both design elements such as borders, highlights, shading, as well as unnecessary labels, legends, and text. More often than not elements such as these detract from the comprehensibility of visual data, whilst adding very little substantial information in return.

Taking a hard anti-chartjunk stance has vastly reduced the amount of time we spend analyzing and discussing visual data. We found we were losing time discussing the pros and cons of design elements rather than the actual take-away from the data itself.

So, whilst chartjunk might make visual data look more pleasing, in some cases, most of the time it simply distracts. We’ve been able to reach conclusions and identify next steps easier and faster by keeping a strict philosophy of design simplicity regarding visual data.”

3. Use purposeful design principles

Another way to avoid cluttering your data reports and dashboards is to start with your end goal in mind. 

“Have a clear goal before you choose the data you want to visualize,” explains Mile Zivkovic of Better Proposals. “Otherwise, you’ll create visualizations, charts, and graphs just for the sake of creating them.”

This applies both to the dashboard design as well as the individual chart level.  

“Picking the best way to visualize your data is a lot like picking out the right outfit – you wouldn’t wear a tuxedo to go hiking and you shouldn’t use a pie chart to analyze a trend,” says Alex Kus of Buddy. “You’ve got to ensure your visual is fit to purpose, keeping your audience in mind and it should have one clear message that you want to be conveyed per graphic. Keep your visualization simple – if you’re like me and have seen one overly complex area chart too many, you’ll know this is important because it makes your data actually possible to parse.

Also, be aware that data visualization does not mean use no text – this is a common mistake of the overly enthusiastic designer, but you should really include labels with some text to bring attention to key points where necessary.”

Brad Touesnard of SpinupWP says, ”If you want to compare values, the best format is to use column charts which allow side-by-side comparisons of different values. Column charts are useful for data, such as website views and sessions, that don’t change much on a day-to-day basis. If you want to analyze a trend, line charts can illustrate how values in one or more categories change over the same period of time.

For example, you can use a line chart to visualize the monthly sales of two different product lines over a year. If you want to show proportions, pie charts are your best choice. For example, if you want to illustrate which campaigns generate the greatest share of your total leads, you can create a pie chart with slices for PPC, SEO, social media, and blogging.”

Ed Cravo of Groundbreaker adds, “It’s often challenging to choose the right visualization for the data you want to show. Before jumping to specifics, think about what you want to accomplish with the visualization, which helps you decide what data to include. 

One size does not fit all so carefully consider and choose the right format for your visualization that will best tell the story and answer key questions generated by data—all of it connecting with your main purpose. 

Once you’ve determined what types of visualizations work best for the data you have collected, it’s time to choose what delivery method makes sense.

If you need to get information out to customers, stakeholders, investors , or employees, an emailed report can be a good method of distribution, especially if this data is being tracked over a period of time where regular updates would be beneficial for your audience. 

If you are using data analysis or a business intelligence platform, there are often in-app visualization options, such as templates or custom dashboards , email or PDF report delivery, and even scheduled ongoing reports. 

If they’re done well, visualizations tell an interesting story. They can also shine a light on hidden information and details that you wouldn’t uncover in a bar chart, or pie graph or stacked bar chart in Google Sheet .”

4. Focus on these three elements 

“I find it useful to keep in mind these three steps when I’m designing information-rich graphics: data, design, and feel,” explains Natasha Rei of Explainerd. “First, the data points need to add up logically and accurately. Check your math! Make sure that your story matches up with what you’re trying to show – if there’s a discrepancy between the data and the caption or scale’s title, then you’ve done something wrong. 

Then step back from just purely making a pretty picture and think about how clear a chart will make a point or tell a story most effectively without too much clutter–are your labels readable? Appropriate headings? A simple graph with plenty of room for viewers’ notes might be better than one overloaded by too many numbers.”

5. Make it easy to compare data 

Data isn’t just pretty charts. It serves a purpose. When designing for executives or investors, that purpose is usually to make it easier to spot trends, patterns, and see correlations. The easier you can make it compare your data, the better. 

“Data visualization is vital to founders in assessing and analyzing company data, making it more valuable and extracting more insights,” says Anton Giuroiu of Homesthetics.net. “One essential advantage of data visualization is determining correlations easier than just plain data. Simple it may sound but column data are appropriate with comparing items and comparing data over time. In highlighting the peaks and trends, you can combine the column chart with the line chart. You can easily say which is higher and which is the highest, without looking at the numerical data.”

For example, Andrew Johnson of Prime Seamless says, “As a business owner, I use data visualization aided by a custom dashboard software to help identify sales trends and be able to compare certain aspects pertaining to my products. I have found that different forms of visual charts serve different purposes. A combination of line and column charts is best suited to help identify trends and to compare items/data over time. This is great for sales reports .

If you need to compare specific items, then a bar chart might be what you need. Personally, my business uses this form of visual data to compare the sales trends of different products and services for multiple age groups. Last but not least, pie charts are a game-changer when it comes to displaying proportional data or percentages. It gives meaningful context to the data and helps quantify the relationship between the data being compared.”

6. Blend your data sources

You also want to make sure your data is accurate, clean, and in a form that makes it compatible for comparing apples to apples. 

“Good data visualization starts with blending all of your data sources in one place, and then display this data in a way that allows you to compare values and analyze trends over time,” says Rod Austin of Localize. “By organizing all of your key metrics with the flexibility to dial in views across multiple sources, you’re able to more effectively address real issues and elevate meaningful priorities from a more comprehensive vantage point.”

Visualize Your Data with Databox

Great data visualizations tell a compelling story. This starts by having a goal in mind and then working backwards to identify the number of charts and different formats you need. The final version can be shared as a report or a dashboard. 

If you are looking for a simple way to share your data visualizations in a dashboard format, get started with Databox for free .

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Data visualization is the representation of data through use of common graphics, such as charts, plots, infographics and even animations. These visual displays of information communicate complex data relationships and data-driven insights in a way that is easy to understand.

Data visualization can be utilized for a variety of purposes, and it’s important to note that is not only reserved for use by data teams. Management also leverages it to convey organizational structure and hierarchy while data analysts and data scientists use it to discover and explain patterns and trends.  Harvard Business Review  (link resides outside ibm.com) categorizes data visualization into four key purposes: idea generation, idea illustration, visual discovery, and everyday dataviz. We’ll delve deeper into these below:

Idea generation

Data visualization is commonly used to spur idea generation across teams. They are frequently leveraged during brainstorming or  Design Thinking  sessions at the start of a project by supporting the collection of different perspectives and highlighting the common concerns of the collective. While these visualizations are usually unpolished and unrefined, they help set the foundation within the project to ensure that the team is aligned on the problem that they’re looking to address for key stakeholders.

Idea illustration

Data visualization for idea illustration assists in conveying an idea, such as a tactic or process. It is commonly used in learning settings, such as tutorials, certification courses, centers of excellence, but it can also be used to represent organization structures or processes, facilitating communication between the right individuals for specific tasks. Project managers frequently use Gantt charts and waterfall charts to illustrate  workflows .  Data modeling  also uses abstraction to represent and better understand data flow within an enterprise’s information system, making it easier for developers, business analysts, data architects, and others to understand the relationships in a database or data warehouse.

Visual discovery

Visual discovery and every day data viz are more closely aligned with data teams. While visual discovery helps data analysts, data scientists, and other data professionals identify patterns and trends within a dataset, every day data viz supports the subsequent storytelling after a new insight has been found.

Data visualization

Data visualization is a critical step in the data science process, helping teams and individuals convey data more effectively to colleagues and decision makers. Teams that manage reporting systems typically leverage defined template views to monitor performance. However, data visualization isn’t limited to performance dashboards. For example, while  text mining  an analyst may use a word cloud to to capture key concepts, trends, and hidden relationships within this unstructured data. Alternatively, they may utilize a graph structure to illustrate relationships between entities in a knowledge graph. There are a number of ways to represent different types of data, and it’s important to remember that it is a skillset that should extend beyond your core analytics team.

Use this model selection framework to choose the most appropriate model while balancing your performance requirements with cost, risks and deployment needs.

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The earliest form of data visualization can be traced back the Egyptians in the pre-17th century, largely used to assist in navigation. As time progressed, people leveraged data visualizations for broader applications, such as in economic, social, health disciplines. Perhaps most notably, Edward Tufte published  The Visual Display of Quantitative Information  (link resides outside ibm.com), which illustrated that individuals could utilize data visualization to present data in a more effective manner. His book continues to stand the test of time, especially as companies turn to dashboards to report their performance metrics in real-time. Dashboards are effective data visualization tools for tracking and visualizing data from multiple data sources, providing visibility into the effects of specific behaviors by a team or an adjacent one on performance. Dashboards include common visualization techniques, such as:

• Tables: This consists of rows and columns used to compare variables. Tables can show a great deal of information in a structured way, but they can also overwhelm users that are simply looking for high-level trends.
• Pie charts and stacked bar charts:  These graphs are divided into sections that represent parts of a whole. They provide a simple way to organize data and compare the size of each component to one other.
• Line charts and area charts:  These visuals show change in one or more quantities by plotting a series of data points over time and are frequently used within predictive analytics. Line graphs utilize lines to demonstrate these changes while area charts connect data points with line segments, stacking variables on top of one another and using color to distinguish between variables.
• Histograms: This graph plots a distribution of numbers using a bar chart (with no spaces between the bars), representing the quantity of data that falls within a particular range. This visual makes it easy for an end user to identify outliers within a given dataset.
• Scatter plots: These visuals are beneficial in reveling the relationship between two variables, and they are commonly used within regression data analysis. However, these can sometimes be confused with bubble charts, which are used to visualize three variables via the x-axis, the y-axis, and the size of the bubble.
• Heat maps:  These graphical representation displays are helpful in visualizing behavioral data by location. This can be a location on a map, or even a webpage.
• Tree maps, which display hierarchical data as a set of nested shapes, typically rectangles. Treemaps are great for comparing the proportions between categories via their area size.

Access to data visualization tools has never been easier. Open source libraries, such as D3.js, provide a way for analysts to present data in an interactive way, allowing them to engage a broader audience with new data. Some of the most popular open source visualization libraries include:

• D3.js: It is a front-end JavaScript library for producing dynamic, interactive data visualizations in web browsers.  D3.js  (link resides outside ibm.com) uses HTML, CSS, and SVG to create visual representations of data that can be viewed on any browser. It also provides features for interactions and animations.
• ECharts:  A powerful charting and visualization library that offers an easy way to add intuitive, interactive, and highly customizable charts to products, research papers, presentations, etc.  Echarts  (link resides outside ibm.com) is based in JavaScript and ZRender, a lightweight canvas library.
• Vega:   Vega  (link resides outside ibm.com) defines itself as “visualization grammar,” providing support to customize visualizations across large datasets which are accessible from the web.
• deck.gl: It is part of Uber's open source visualization framework suite.  deck.gl  (link resides outside ibm.com) is a framework, which is used for  exploratory data analysis  on big data. It helps build high-performance GPU-powered visualization on the web.

With so many data visualization tools readily available, there has also been a rise in ineffective information visualization. Visual communication should be simple and deliberate to ensure that your data visualization helps your target audience arrive at your intended insight or conclusion. The following best practices can help ensure your data visualization is useful and clear:

Set the context: It’s important to provide general background information to ground the audience around why this particular data point is important. For example, if e-mail open rates were underperforming, we may want to illustrate how a company’s open rate compares to the overall industry, demonstrating that the company has a problem within this marketing channel. To drive an action, the audience needs to understand how current performance compares to something tangible, like a goal, benchmark, or other key performance indicators (KPIs).

Know your audience(s): Think about who your visualization is designed for and then make sure your data visualization fits their needs. What is that person trying to accomplish? What kind of questions do they care about? Does your visualization address their concerns? You’ll want the data that you provide to motivate people to act within their scope of their role. If you’re unsure if the visualization is clear, present it to one or two people within your target audience to get feedback, allowing you to make additional edits prior to a large presentation.

Choose an effective visual:  Specific visuals are designed for specific types of datasets. For instance, scatter plots display the relationship between two variables well, while line graphs display time series data well. Ensure that the visual actually assists the audience in understanding your main takeaway. Misalignment of charts and data can result in the opposite, confusing your audience further versus providing clarity.

Keep it simple:  Data visualization tools can make it easy to add all sorts of information to your visual. However, just because you can, it doesn’t mean that you should! In data visualization, you want to be very deliberate about the additional information that you add to focus user attention. For example, do you need data labels on every bar in your bar chart? Perhaps you only need one or two to help illustrate your point. Do you need a variety of colors to communicate your idea? Are you using colors that are accessible to a wide range of audiences (e.g. accounting for color blind audiences)? Design your data visualization for maximum impact by eliminating information that may distract your target audience.

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The Ultimate Guide to Data Visualization

Data visualization is important because it breaks down complex data and extracts meaningful insights in a more digestible way. Displaying the data in a more engaging way helps audiences make sense of the information with a higher chance of retention. But with a variety of charts and graphs, how can you tell which is best for your specific content and audience?

Consider this your ultimate guide to data visualization. We’re breaking down popular charts and graphs and explaining the differences between each so that you can choose the best slide for your story. 

Charts vs. graphs

We know that numbers don’t lie and are a strong way to back up your story, but that doesn’t always mean they’re easy to understand. By packaging up complex numbers and metrics in visually appealing graphics you’re telling your audience exactly what they need to know without having to rack their brain to comprehend it. Graphs and charts are important in your presentation because they take your supporting statistics, and story, and make them more relatable. 

Charts present data or complex information through tables, infographics , and diagrams, while graphs show a connection between two or more sets of data.

A histogram is a visual representation of the distribution of data. The graph itself consists of a set of rectangles— each rectangle represents a range of values (called a "bin"), while the height corresponds to the numbers of the data that fall within that range.

Histograms are oftentimes used to visualize the frequency distribution of continuous data. Things such as measurements of height, weight, or time can all be organized in the graph. They can also be used to display the distribution of discrete data, like the number of shoes sold in a shoe department during any given period of time.

Histograms are a useful tool for analyzing data, as they allow you to quickly see the shape of the data distribution, the location of the central tendency (the mean or median), and the full spread of the data. They’re a great chart that can also reveal any changes in the data, making it easier to digest.

Need to add a little visual interest to your business presentation? A bar graph slide can display your data easily and effectively. Whether you use a vertical bar graph or horizontal bar graph, a bar graph gives you options to help simplify and present complex data, ensuring you get your point across.

Use it to track long-term changes.

Vertical bar graphs are great for comparing different groups that change over a long period of time. Small or short-term changes may not be as obvious in bar graph form.

Don’t be afraid to play with design .

You can use one bar graph template slide to display a lot of information, as long as you differentiate between data sets. Use colors, spacing, and labels to make the differences obvious.

Use a horizontal graph when necessary.

If your data labels are long, a horizontal bar graph may be easier to read and organize than a vertical bar graph. 

Don’t use a horizontal graph to track time.

A vertical bar graph makes more sense when graphing data over time, since the x-axis is usually read from left to right.

Histograms vs. bar graphs

While a histogram is similar to a bar graph, it groups numbers into ranges and displays data in a different way.

Bar graphs are used to represent categorical data, where each bar represents a different category with a height or length proportional to the associated value. The categories of a bar graph don’t overlap, and the bars are usually separated by a gap to differentiate from one another. Bar graphs are ideal when you need to compare the data of different categories.

On the other hand, histograms divide data into a set of intervals or "bins". The bars of a histogram are typically adjacent to each other, with no gaps, as the bins are continuous and can overlap. Histograms are used to visualize the shape, center, and spread of a distribution of numerical data.

A pie chart is a circular graph (hence the name ‘pie’) that’s used to show or compare different segments — or ‘slices’ — of data. Each slice represents a proportion that relates to the whole. When added up, each slice should equal the total. Pie charts are best used for showcasing part-to-whole relationships. In other words, if you have different parts or percentages of a whole, using a pie chart is likely the way to go. Just make sure the total sum equals 100%, or the chart won’t make a lot of sense or convey the message you want it to. Essentially, any type of content or data that can be broken down into comparative categories is suitable to use. Revenue, demographics, market shares, survey results — these are just a few examples of the type of content to use in a pie chart. However, you don’t want to display more than six categories of data or the pie chart can be difficult to read and compare the relative size of slices. 

Donut Charts

A donut chart is almost identical to a pie chart, but the center is cut out (hence the name ‘donut’). Donut charts are also used to show proportions of categories that make up the whole, but the center can also be used to display data. Like pie charts, donut charts can be used to display different data points that total 100%. These are also best used to compare a handful of categories at-a-glance and how they relate to the whole. The same type of content you’d use for a pie chart can also work for a donut chart. However, with donut charts, you have room for fewer categories than pie charts — anywhere from 2 to 5. That’s because you want your audience to be able to quickly tell the difference between arc lengths, which can help tell a more compelling story and get your point across more efficiently. 

Pie charts vs. donut charts

You may notice that a donut chart and a pie chart look almost identical . While a donut chart is essentially the same as a pie chart in function, with its center cut out, the “slices” in a donut chart are sometimes more clearly defined than in a pie chart.

When deciding between a pie chart or a donut chart for your presentation, make sure the data you’re using is for comparison analysis only. Pie and donut charts are usually limited to just that — comparing the differences between categories. The easiest way to decide which one to use? 

The number of categories you’re comparing. If you have more than 4 or 5 categories, go with a pie chart. If you have between 2 and 4 categories, go with a donut chart. Another way to choose? If you have an extra data point to convey (e.g. all of your categories equal an increase in total revenue), use a donut chart so you can take advantage of the space in the middle.

Comparison charts

As its name implies, a comparison chart or comparison graph draws a comparison between two or more items across different parameters. You might use a comparison chart to look at similarities and differences between items, weigh multiple products or services in order to choose one, or present a lot of data in an easy-to-read format.

For a visually interesting twist on a plain bar chart, add a data comparison slide to your presentation. Our data comparison template is similar to a bar graph, using bars of varying lengths to display measured data. The data comparison template, however, displays percentages instead of exact numbers. One of the best things about using Beautiful.ai’s data comparison slide? You can customize it for your presentation. Create a horizontal or vertical slide, remove or add grid lines, play with its design, and more.

Gantt charts

A Gantt chart , named after its early 20th century inventor Henry Gantt, is a birds-eye view of a project. It visually organizes tasks displayed over time. Gantt charts are incredibly useful tools that work for projects and groups of all sizes. 

It’s a type of bar chart that you would use to show the start and finish dates of several elements of a project such as what the project tasks are, who is working on each task, how long each task will take, and how tasks group together, overlap, and link with each other. The left side of a Gantt chart lists each task in a project by name. Running along the top of the chart from left to right is a timeline. Depending on the demands and details of your project, the timeline may be broken down by quarter, month, week, or even day.

Project management can be complex, so it’s important to keep your chart simple by using a color scheme with cool colors like blues or greens. You can color code items thematically or by department or person, or even highlight a single task with a contrasting color to call attention to it. You can also choose to highlight important tasks using icons or use images for other annotations. This will make your chart easier to read and more visually appealing. 

Additional tips for creating an effective Gantt chart slide .

Use different colors

How many colors you use and how you assign them is up to you. You might choose one color to represent a specific team or department so that you can see who is responsible for which tasks on your chart, for example. 

Set milestones

Don’t forget to set milestones where they make sense: deadlines required by clients or customers, when a new department takes over the next phase of the project, or when a long list of tasks is completed. 

Label your tasks

When used with a deliberate color scheme, labeling your tasks with its project owner will prevent confusion and make roles clear to everyone. 

Jordan Turner

Jordan is a Bay Area writer, social media manager, and content strategist.

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Visualisation: visual representations of data and information

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Course content, course reviews, course learning outcomes.

After studying this course, you should be able to:

• understand what is meant by the term 'visualisation' within the context of data and information
• interpret and create a range of visual representations of data and information
• recognise a range of visualisation models such as cartograms, choropleth maps and hyperbolic trees
• select an appropriate visualisation model to represent a given data set
• recognise when visualisations are presenting information in a misleading way.

First Published: 09/08/2012

Updated: 11/06/2019

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16 Best Types of Charts and Graphs for Data Visualization [+ Guide]

Published: June 08, 2023

There are more type of charts and graphs than ever before because there's more data. In fact, the volume of data in 2025 will be almost double the data we create, capture, copy, and consume today.

This makes data visualization essential for businesses. Different types of graphs and charts can help you:

• Motivate your team to take action.
• Impress stakeholders with goal progress.
• Show your audience what you value as a business.

Data visualization builds trust and can organize diverse teams around new initiatives. Let's talk about the types of graphs and charts that you can use to grow your business.

Free Excel Graph Templates

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Click this link to access this resource at any time.

Different Types of Graphs for Data Visualization

1. bar graph.

A bar graph should be used to avoid clutter when one data label is long or if you have more than 10 items to compare.

Best Use Cases for These Types of Graphs

Bar graphs can help you compare data between different groups or to track changes over time. Bar graphs are most useful when there are big changes or to show how one group compares against other groups.

The example above compares the number of customers by business role. It makes it easy to see that there is more than twice the number of customers per role for individual contributors than any other group.

A bar graph also makes it easy to see which group of data is highest or most common.

For example, at the start of the pandemic, online businesses saw a big jump in traffic. So, if you want to look at monthly traffic for an online business, a bar graph would make it easy to see that jump.

Other use cases for bar graphs include:

• Product comparisons.
• Product usage.
• Category comparisons.
• Marketing traffic by month or year.
• Marketing conversions.

Design Best Practices for Bar Graphs

• Use consistent colors throughout the chart, selecting accent colors to highlight meaningful data points or changes over time.
• Use horizontal labels to improve readability.
• Start the y-axis at 0 to appropriately reflect the values in your graph.

2. Line Graph

A line graph reveals trends or progress over time, and you can use it to show many different categories of data. You should use it when you chart a continuous data set.

Line graphs help users track changes over short and long periods. Because of this, these types of graphs are good for seeing small changes.

Line graphs can help you compare changes for more than one group over the same period. They're also helpful for measuring how different groups relate to each other.

A business might use this graph to compare sales rates for different products or services over time.

These charts are also helpful for measuring service channel performance. For example, a line graph that tracks how many chats or emails your team responds to per month.

Design Best Practices for Line Graphs

• Use solid lines only.
• Don't plot more than four lines to avoid visual distractions.
• Use the right height so the lines take up roughly 2/3 of the y-axis' height.

3. Bullet Graph

A bullet graph reveals progress towards a goal, compares this to another measure, and provides context in the form of a rating or performance.

In the example above, the bullet graph shows the number of new customers against a set customer goal. Bullet graphs are great for comparing performance against goals like this.

These types of graphs can also help teams assess possible roadblocks because you can analyze data in a tight visual display.

For example, you could create a series of bullet graphs measuring performance against benchmarks or use a single bullet graph to visualize these KPIs against their goals:

• Customer satisfaction.
• Average order size.
• New customers.

Seeing this data at a glance and alongside each other can help teams make quick decisions.

Bullet graphs are one of the best ways to display year-over-year data analysis. You can also use bullet graphs to visualize:

• Customer satisfaction scores.
• Customer shopping habits.
• Social media usage by platform.

Design Best Practices for Bullet Graphs

• Use contrasting colors to highlight how the data is progressing.
• Use one color in different shades to gauge progress.

Different Types of Charts for Data Visualization

To better understand these chart types and how you can use them, here's an overview of each:

1. Column Chart

Use a column chart to show a comparison among different items or to show a comparison of items over time. You could use this format to see the revenue per landing page or customers by close date.

Best Use Cases for This Type of Chart

You can use both column charts and bar graphs to display changes in data, but column charts are best for negative data. The main difference, of course, is that column charts show information vertically while bar graphs show data horizontally.

For example, warehouses often track the number of accidents on the shop floor. When the number of incidents falls below the monthly average, a column chart can make that change easier to see in a presentation.

In the example above, this column chart measures the number of customers by close date. Column charts make it easy to see data changes over a period of time. This means that they have many use cases, including:

• Customer survey data, like showing how many customers prefer a specific product or how much a customer uses a product each day.
• Sales volume, like showing which services are the top sellers each month or the number of sales per week.
• Profit and loss, showing where business investments are growing or falling.

Design Best Practices for Column Charts

2. dual-axis chart.

A dual-axis chart allows you to plot data using two y-axes and a shared x-axis. It has three data sets. One is a continuous data set, and the other is better suited to grouping by category. Use this chart to visualize a correlation or the lack thereof between these three data sets.

A dual-axis chart makes it easy to see relationships between different data sets. They can also help with comparing trends.

For example, the chart above shows how many new customers this company brings in each month. It also shows how much revenue those customers are bringing the company.

This makes it simple to see the connection between the number of customers and increased revenue.

You can use dual-axis charts to compare:

• Price and volume of your products.
• Revenue and units sold.
• Sales and profit margin.
• Individual sales performance.

Design Best Practices for Dual-Axis Charts

• Use the y-axis on the left side for the primary variable because brains naturally look left first.
• Use different graphing styles to illustrate the two data sets, as illustrated above.
• Choose contrasting colors for the two data sets.

3. Area Chart

An area chart is basically a line chart, but the space between the x-axis and the line is filled with a color or pattern. It is useful for showing part-to-whole relations, like showing individual sales reps’ contributions to total sales for a year. It helps you analyze both overall and individual trend information.

Best Use Cases for These Types of Charts

Area charts help show changes over time. They work best for big differences between data sets and help visualize big trends.

For example, the chart above shows users by creation date and life cycle stage.

A line chart could show more subscribers than marketing qualified leads. But this area chart emphasizes how much bigger the number of subscribers is than any other group.

These charts make the size of a group and how groups relate to each other more visually important than data changes over time.

Area graphs can help your business to:

• Visualize which product categories or products within a category are most popular.
• Show key performance indicator (KPI) goals vs. outcomes.
• Spot and analyze industry trends.

Design Best Practices for Area Charts

• Use transparent colors so information isn't obscured in the background.
• Don't display more than four categories to avoid clutter.
• Organize highly variable data at the top of the chart to make it easy to read.

4. Stacked Bar Chart

Use this chart to compare many different items and show the composition of each item you’re comparing.

These graphs are helpful when a group starts in one column and moves to another over time.

For example, the difference between a marketing qualified lead (MQL) and a sales qualified lead (SQL) is sometimes hard to see. The chart above helps stakeholders see these two lead types from a single point of view — when a lead changes from MQL to SQL.

Stacked bar charts are excellent for marketing. They make it simple to add a lot of data on a single chart or to make a point with limited space.

These graphs can show multiple takeaways, so they're also super for quarterly meetings when you have a lot to say but not a lot of time to say it.

Stacked bar charts are also a smart option for planning or strategy meetings. This is because these charts can show a lot of information at once, but they also make it easy to focus on one stack at a time or move data as needed.

You can also use these charts to:

• Show the frequency of survey responses.
• Identify outliers in historical data.
• Compare a part of a strategy to its performance as a whole.

Design Best Practices for Stacked Bar Graphs

• Best used to illustrate part-to-whole relationships.
• Use contrasting colors for greater clarity.
• Make the chart scale large enough to view group sizes in relation to one another.

5. Mekko Chart

Also known as a Marimekko chart, this type of graph can compare values, measure each one's composition, and show data distribution across each one.

It's similar to a stacked bar, except the Mekko's x-axis can capture another dimension of your values — instead of time progression, like column charts often do. In the graphic below, the x-axis compares the cities to one another.

Image Source

You can use a Mekko chart to show growth, market share, or competitor analysis.

For example, the Mekko chart above shows the market share of asset managers grouped by location and the value of their assets. This chart clarifies which firms manage the most assets in different areas.

It's also easy to see which asset managers are the largest and how they relate to each other.

Mekko charts can seem more complex than other types of charts and graphs, so it's best to use these in situations where you want to emphasize scale or differences between groups of data.

Other use cases for Mekko charts include:

• Detailed profit and loss statements.
• Revenue by brand and region.
• Product profitability.
• Share of voice by industry or niche.

Design Best Practices for Mekko Charts

• Vary your bar heights if the portion size is an important point of comparison.
• Don't include too many composite values within each bar. Consider reevaluating your presentation if you have a lot of data.
• Order your bars from left to right in such a way that exposes a relevant trend or message.

6. Pie Chart

A pie chart shows a static number and how categories represent part of a whole — the composition of something. A pie chart represents numbers in percentages, and the total sum of all segments needs to equal 100%.

The image above shows another example of customers by role in the company.

The bar graph example shows you that there are more individual contributors than any other role. But this pie chart makes it clear that they make up over 50% of customer roles.

Pie charts make it easy to see a section in relation to the whole, so they are good for showing:

• Customer personas in relation to all customers.
• Revenue from your most popular products or product types in relation to all product sales.
• Percent of total profit from different store locations.

Design Best Practices for Pie Charts

• Don't illustrate too many categories to ensure differentiation between slices.
• Ensure that the slice values add up to 100%.
• Order slices according to their size.

7. Scatter Plot Chart

A scatter plot or scattergram chart will show the relationship between two different variables or reveal distribution trends.

Use this chart when there are many different data points, and you want to highlight similarities in the data set. This is useful when looking for outliers or understanding your data's distribution.

Scatter plots are helpful in situations where you have too much data to see a pattern quickly. They are best when you use them to show relationships between two large data sets.

In the example above, this chart shows how customer happiness relates to the time it takes for them to get a response.

This type of graph makes it easy to compare two data sets. Use cases might include:

• Employment and manufacturing output.
• Retail sales and inflation.
• Visitor numbers and outdoor temperature.
• Sales growth and tax laws.

Try to choose two data sets that already have a positive or negative relationship. That said, this type of graph can also make it easier to see data that falls outside of normal patterns.

Design Best Practices for Scatter Plots

• Include more variables, like different sizes, to incorporate more data.
• Start the y-axis at 0 to represent data accurately.
• If you use trend lines, only use a maximum of two to make your plot easy to understand.

8. Bubble Chart

A bubble chart is similar to a scatter plot in that it can show distribution or relationship. There is a third data set shown by the size of the bubble or circle.

In the example above, the number of hours spent online isn't just compared to the user's age, as it would be on a scatter plot chart.

Instead, you can also see how the gender of the user impacts time spent online.

This makes bubble charts useful for seeing the rise or fall of trends over time. It also lets you add another option when you're trying to understand relationships between different segments or categories.

For example, if you want to launch a new product, this chart could help you quickly see your new product's cost, risk, and value. This can help you focus your energies on a low-risk new product with a high potential return.

You can also use bubble charts for:

• Top sales by month and location.
• Customer satisfaction surveys.
• Store performance tracking.
• Marketing campaign reviews.

Design Best Practices for Bubble Charts

• Scale bubbles according to area, not diameter.
• Make sure labels are clear and visible.
• Use circular shapes only.

9. Waterfall Chart

Use a waterfall chart to show how an initial value changes with intermediate values — either positive or negative — and results in a final value.

Use this chart to reveal the composition of a number. An example of this would be to showcase how different departments influence overall company revenue and lead to a specific profit number.

The most common use case for a funnel chart is the marketing or sales funnel. But there are many other ways to use this versatile chart.

If you have at least four stages of sequential data, this chart can help you easily see what inputs or outputs impact the final results.

For example, a funnel chart can help you see how to improve your buyer journey or shopping cart workflow. This is because it can help pinpoint major drop-off points.

Other stellar options for these types of charts include:

• Deal pipelines.
• Conversion and retention analysis.
• Bottlenecks in manufacturing and other multi-step processes.
• Marketing campaign performance.
• Website conversion tracking.

Design Best Practices for Funnel Charts

• Scale the size of each section to accurately reflect the size of the data set.
• Use contrasting colors or one color in graduated hues, from darkest to lightest, as the size of the funnel decreases.

11. Heat Map

A heat map shows the relationship between two items and provides rating information, such as high to low or poor to excellent. This chart displays the rating information using varying colors or saturation.

Best Use Cases for Heat Maps

In the example above, the darker the shade of green shows where the majority of people agree.

With enough data, heat maps can make a viewpoint that might seem subjective more concrete. This makes it easier for a business to act on customer sentiment.

There are many uses for these types of charts. In fact, many tech companies use heat map tools to gauge user experience for apps, online tools, and website design .

Another common use for heat map graphs is location assessment. If you're trying to find the right location for your new store, these maps can give you an idea of what the area is like in ways that a visit can't communicate.

Heat maps can also help with spotting patterns, so they're good for analyzing trends that change quickly, like ad conversions. They can also help with:

• Competitor research.
• Customer sentiment.
• Sales outreach.
• Campaign impact.
• Customer demographics.

Design Best Practices for Heat Map

• Use a basic and clear map outline to avoid distracting from the data.
• Use a single color in varying shades to show changes in data.
• Avoid using multiple patterns.

12. Gantt Chart

The Gantt chart is a horizontal chart that dates back to 1917. This chart maps the different tasks completed over a period of time.

Gantt charting is one of the most essential tools for project managers. It brings all the completed and uncompleted tasks into one place and tracks the progress of each.

While the left side of the chart displays all the tasks, the right side shows the progress and schedule for each of these tasks.

This chart type allows you to:

• Break projects into tasks.
• Track the start and end of the tasks.
• Set important events, meetings, and announcements.
• Assign tasks to the team and individuals.

Download the Excel templates mentioned in the video here.

5 Questions to Ask When Deciding Which Type of Chart to Use

1. do you want to compare values.

Charts and graphs are perfect for comparing one or many value sets, and they can easily show the low and high values in the data sets. To create a comparison chart, use these types of graphs:

• Scatter plot

2. Do you want to show the composition of something?

Use this type of chart to show how individual parts make up the whole of something, like the device type used for mobile visitors to your website or total sales broken down by sales rep.

To show composition, use these charts:

• Stacked bar

3. Do you want to understand the distribution of your data?

Distribution charts help you to understand outliers, the normal tendency, and the range of information in your values.

Use these charts to show distribution:

4. Are you interested in analyzing trends in your data set?

If you want more information about how a data set performed during a specific time, there are specific chart types that do extremely well.

You should choose one of the following:

• Dual-axis line

5. Do you want to better understand the relationship between value sets?

Relationship charts can show how one variable relates to one or many different variables. You could use this to show how something positively affects, has no effect, or negatively affects another variable.

When trying to establish the relationship between things, use these charts:

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How to Represent Linear Data Visually for Information Visualization

Linear data sets come in three varieties – univariate, bivariate and trivariate. A univariate data set has a single dependent variable which varies compared to the independent attributes of that data. A bivariate set has two dependent variables and a trivariate set has three.

Representing data sets of these types is a very common task for the information visualization designer. Choosing the right form of visualization will depend on the requirements of the user of the visualization as much as the data set itself.

Univariate Data Sets

Univariate data sets are very simple to represent visually. There are many different forms for showing univariate data and one of the most common is tabulating the data itself. For example you might want to show the relationship between types of car and their top speed.

Note: According to Automoblog.net these were the 10 fastest cars in the world in 2007.

Stephen Few, the information visualization consultant said; “Numbers have an important story to tell. They rely on you to give them a clear and convincing voice.” There is nothing intrinsically wrong with presenting this data in a tabulated format. It may be exactly what the user needs . Certainly, you can fairly quickly determine which car is the fastest (the Hennessey Venom GT) from the table – even if it’s not immediately obvious which is the fastest at first glance.

However, it might be better to represent the data graphically which would enable an easier comparison between all the cars.

There are many different graphical representations that can be used to show univariate data. They include pie charts, histograms, scatter plots, bar graphs, etc. The precise nature of the representation chosen will depend on the data set.

For example here’s the speed data shown as a pie chart:

Not very useful is it? There’s too much data of a similar nature and the color key is equally confusing. You can’t tell very much about the cars and their relative speeds from a pie chart.

Whereas here’s the same data set represent as a bar graph:

It is instantly clear from the bar graph which vehicle is the fastest and which is the slowest and how each compares to every other vehicle.

Bivariate Data Sets

Bivariate data sets have two sets of dependent variables that we wish to compare against the independent variable(s).

So let’s take our original data set and expand it to include the horsepower of each vehicle.

Now we might want to examine the relationship between the speed of each vehicle and the horsepower that powers the vehicle. Does an increase in horsepower automatically mean an increase in the speed of the vehicle? Are they proportionate to each other?

Again, there are many different ways to represent the data. We’ve chosen a an area driven graph with the two data sets imposed on top of each other.

From this graph it’s very easy to conclude that while the speeds of the vehicles don’t vary dramatically – the horse power of each vehicle does. Yes, there’s definitely a case to be made that large amounts of horsepower do correlate with speed but it’s only a weak correlation. The McLaren F1 is faster than the Aston Martin One-77 but carries markedly less horsepower, for example.

It’s worth noting that caution must be taken when choosing bivariate representations. In this graph, we have focused our attention on the horsepower of the vehicles and the similarity between the top speeds of the vehicles. However, there is greater variation in the speeds than you can tell by glancing at the graph – it serves our purpose for analysis but is not necessarily the perfect representation for other purposes.

It could also be argued that the third-dimension, in our representation, adds little value to the viewer and could be eliminated in favor of a flatter representation.

Trivariate Data Sets

A trivariate data set includes a third dependent variable that can be represented against the independent variable(s).

For example we might want to include a stopping distance within our car data set (please note that these distances were created for this example and are not likely to be the actual stopping distances of these vehicles) :

Once again trivariate data can be represented in any number of ways. However, a common methodology is the 3D scatter plot as shown above.

Again, caution must be taken when choosing the representation for trivariate data sets. Two common problems with models here are occlusion (where one item in a data set is obscured by another – so you cannot see its actual place in the model) and the fact that it can be hard to determine where, exactly, along any given axis the data point lays.

It’s for this reason that trivariate models are often interactive and can be manipulated by the user to be viewed from different angles in order to gain a better understanding of the data.

A Practical Tip

There is no usual reason to create your models by hand; most spreadsheet packages (such as Excel) can create univariate and bivariate models with ease from tabulated data. There are also specialist software modeling packages for more complex models including trivariate data sets.

You may decide to create the model in one package and then, for reasons of aesthetics , recreate the model in a graphic design package as the design elements in Excel, for example, are somewhat limited.

The Take Away

The key to creating visual representations of linear data is to ensure the usability of the final representation. Fortunately, you do not have to create these models “from scratch” but can use computer tools to do the job for you. This allows you to quickly switch between models until you find one that is fit for purpose.

References & Where to Learn More:

10 Fastest cars in the world.

Stephen Few – Show Me Numbers Designing Tables and Graphs to Enlighten – Analytics Press, ISB 978-0970601971

Hero Image: Author/Copyright holder: Eric Fischer. Copyright terms and licence: CC BY 2.0

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What is a data display? Definition, Types, & Examples

You may have heard that “data is the new oil” — the most valuable commodity of the 21st century. But just like oil is useless until refined, data is useless until simplified and communicated. Data displays are a tool to help analysts do just that.

Because they’re so important to data, data displays can be found in virtually every discipline that deals with large amounts of information. Consequently, the precise meaning behind data displays has become blurred, resulting in a lot of unanswered questions — many of which you may have already asked yourself.

The purpose of this article is to clear things up. I will briefly define data displays, show examples of 17 popular displays, answer some common questions, compare data displays and data visualization, cover data displays in the context of qualitative data, and explore common data visualization tools.

Don’t forget, you can get the free Intro to Data Analysis eBook to get a strong start.

Data Display Definition

Also known as data visualization, a data display is a visual representation of raw or processed data that aims to communicate a small number of insights about the behavior of an underlying table, which is otherwise difficult or impossible to understand to the naked eye. Common examples include graphs and charts, but any visual depiction of information, even maps, can be considered data displays.

Additionally, the term “data display” can refer to a legal agreement in which a publishing entity, often a stock exchange, obtains the rights from a partner to publicly display the partner’s data. This is important to know, but it’s outside the scope of this article.

Types of Data Display: 17 Actionable Visualizations with Examples

The most common types of data displays are the 17 that follow:

• bar charts,
• column charts,
• stacked bar charts,
• line graphs,
• area charts,
• stacked area charts,
• unstacked area charts,
• combo bar-and-line charts,
• waterfall charts,
• tree diagrams,
• bullet graphs,
• scatter plots,
• histograms,
• packed bubble charts, and
• box & whisker plots.

Let’s look at each of these with an example. I’ll be using Tableau software to show these, but many of them are available in Excel.

Bar charts show the value of dimensions as horizontal rectangles. They’re useful for comparing simple items side-by-side. This image shows total checkouts for two book IDs.

Column Charts

Column charts show the value of dimensions as a vertical rectangle. Like bar charts, they’re useful for comparing simple items side-by-side. This image shows total checkouts for two book IDs.

Stacked Bar/Stacked Columns Charts

Stacked bar or column charts show the value of dimensions with more granular dimensions “inside.” They’re useful for comparing dimensions with additional breakdown. In this image, the columns represent total checkouts by book ID, and the colors represent month of checkout.

Tree maps show the value of multiple dimensions by their relative size and splits them into rectangles in a “spiral” fashion. As you can see here, book IDs are shows in size by the number of checkouts they had.

Line Graphs

Line graphs show the value of two dimensions that are continuous, most often wherein one of the dimensions is time. This image shows five book IDs by number of checkouts over time.

Area Charts

Area charts show the value of a dimension as all the space under a line (often over time).

Stacked Area Charts

Stacked are charts show the value of two dimension values as areas stacked on top of each other, such that one starts where the other ends on the vertical axis.

Unstacked Area Charts

Unstacked area charts show two area charts layered on top of each other such that both start from zero. As you can see below, this view is useful for comparing the sum of two values over time.

Combo Bar-and-Line Charts

A bar-and-line chart shows two different measures — one as a line and the other as bars. These are particularly useful when showing a running total as a line and the individual values of the total as bars.

Waterfall Charts

Waterfall charts show a beginning balance, additions, subtractions, and an ending balance, all as a sequence of connected bars. These are useful for showing additions and subtractions, or a corkscrew calculations, around a project or account.

Tree Diagrams

Tree diagrams show the hierarchical relationship between elements of a system.

Bullet Graphs

Bullet graphs show a column value of actual real numbers (blue bars), a marker for a target number (the small black vertical lines), and shading at different intervals to indicate quality of performance such as bad, acceptable, and good.

Scatter Plots

Scatter plots show points on a plane where two variables meet — very similar to a line graph but used to compare any kind of variables, not just a value over time.

Histograms show bars representing groupings of a given dimension. This is easier to understand in the picture — each column represents a number of entries that fall into a range, i.e. 10 values fall into a bin ranging from 1-4, 29 values fall into a bin of 5-8, etc.

Heat maps show the intensity of a grid of values through the use of color shading and size intensities.

Packed Bubble Charts

Packed bubble charts show the intensity of dimension values based on relative size of “bubbles,” which are nothing more than circles.

Box & Whisker Plots

Box & whisker plots show values of a series based on 4 markers: max, min, lower 25% quartile, upper 75% quartile, and the average.

Don’t Use Pie Charts!

One of the most common chart types is a pie chart, and I’m asking you to never use one. Why? Because pie charts don’t provide any value to the viewer.

A pie chart shows the percent that parts of a total represent. But what does that mean for the viewer? Visually, it’s difficult to distinguish which slices are largest, unless you have one slice that dominates 80% or more of the pie — or you use labels on each slice.

If you want to show percent of total, use a percent of total bar chart. Or better yet, use a waterfall chart! These will be much more informative to the viewer.

Packed Bubble Charts: the New Cool Thing

I’m not totally against bubble charts, but they’re not the most insightful visual we can provide. A bubble chart has no structure, so it’s not possible to compare different values. They’re similar to pie charts in that it’s difficult to draw insight.

That said, there is some creative value to the viewer. Bubble charts grab attention, which means you can use them to draw in users and show them more insightful charts.

Why display data in different ways?

In all of the example charts above, I used the same two data tables. What this means is that any given data set can be represented in many different data displays . So why would we represent data in different ways?

The simple answer is that it helps the viewer think differently about information . When I showed the stacked area chart of number of checkouts for two different books, it appeared as though the books followed the same trend.

However, when I showed them in an unstacked view, we clearly see that the book colored orange performed slightly better in Q1 – Q3, whereas the book colored blue performed better in Q4.

Displaying data in different ways allows us to think differently about it — to gain insights and understand it in new and creative ways.

Another reason for using multiple data displays is for an analyst to cater to his/her audience . For example, take another look at the bullet graph and scatter plot above.

Managers in a book selling firm are likely very interested in the performance of sales in Q1 vs Q2, so the bullet graph is better for them . However, a writer looking to better understand the relationship between sales of individual books in Q1 vs Q2 will prefer the scatter plot .

Which data display shows the number of observations?

I’m not sure where this question comes from, but it’s asked a lot. An observation is nothing more than one line in a data table, and many wonder what data display shows the total number of these lines.

In short, any data display can show the number of observations in the underlying data set — it’s only a question of granularity of dimensions. However, the most common data display showing number of observations is a scatter plot. As long as you include a measure at the observation level of detail, the scatter will show the number of observations.

If the goal, however, is simply to count the number of observations, most data table software have a simple count function . In Excel, it’s COUNTA(array of one column). In Tableau, it’s COUNT([observation metric]).

What data display uses intervals and frequency?

Another common question, and this one is easy. Take another look at the histogram above. It pinpoints intervals and counts the number of records within that interval. The number of records is also know as frequency. In short, the data display showing intervals and frequency is a histogram.

Which data display is misleading?

You may have heard the term “misleading data.” Unfortunately, misleading data is a necessary evil in the world of informatics. While any data display can be misleading, the most common examples are bar charts in which an axis is made non-zero and line charts in which the data axis (x-axis) is reversed. The first results in an inflated visual value of bars, and the second results in the reverse interpretation of a trend over time.

Misleading data is a huge topic and is outside the scope of this article. If you’re interested in it, check out these articles:

• Data Distortion: What is it? And how is it misleading?
• Pros & Cons of Data Visualization: the Good, Bad, & Ugly

Data Visualization vs Data Display

Alas, we arrive at what is likely the most common source of confusion surrounding data displays: the difference between a data display and a data visualization.

In most cases, there is no difference between a data visualization and a data display — they are synonyms. However, the term “visualization” is a buzzword that invites the image of aesthetically-pleasing data displays, whereas “data display” can refer to visualizations OR aesthetically-simple charts and graphs like those used in academic papers.

What is a data display in qualitative research?

Since data is quantitative, applying data displays to qualitative research can be challenging — but it’s 100% possible. It requires converting qualitative data into quantitative data . In most cases qualitative data consists of words, so “conversion” involves counting words. In practice, counting manifests as (1) idea coding and/or (2) determining word frequency .

Idea coding consists of reading through text and assigning designated phrases per idea covered, then counting the number of times these phrases appear . Word frequency consists of passing a text through a word analyzer software and counting the most common combinations . The details around these techniques are outside the scope of this article, but you can learn more in the article Qualitative Content Analysis: a Simple Guide with Examples .

Once converted into numbers, we can display qualitative data just like we display quantitative data in the 17 Actionable Visualizations . So, how do we answer the question “what is a data display in qualitative research?”

In short, a data display in qualitative research is the visualization of words after they have been quantified through idea coding, word frequency, or both.

Data Display Tools and Products (5 Examples)

Any article on data displays worth its salt shows data tools. Here are five free data visualization tools you can get started with today.

Admittedly, Excel is not a data display tool in the strict sense of the term. However, it offers several user-friendly visualization options. You can navigate to them via the Insert ribbon. The options include:

• Column charts,
• Bar charts,
• Line graphs,
• Histograms,
• Box & Whisker Plots,
• Waterfall charts,
• Pie charts (but don’t use them!),
• Scatter plots, and
• Combo charts

They’re displayed in the icons as shown in the below picture:

Tableau is the leading data visualization software, and for good reason. It’s what I used to build all of the data displays earlier in this article. Tableau interacts directly with data stored in Excel, on a local server, in Google Sheets, and many other sources.

It provides one of the most flexible interfaces available, allowing you to rapidly “slice and dice” different dimensions and measures and switch between visualizations with the click of a button.

The one downside is that Tableau takes some time to learn . Its flexibility requires the use of many functional buttons, and you’ll need some time to understand them.

You can download the free version of the paid product called Tableau Public .

I only recently learned about Flourish. It’s a pre-set data display tool that’s much less flexible than Tableau, but much easier to get started on. Given a set of static and dynamic charts to choose from, Flourish prompts you to fill in data in a format compatible with the chart.

Have you seen those “rat race” videos where GDP per country or market cap by company is shown over time? With the leaders moving to the top over the years? You can build that in Flourish .

Infogram allows the creation of a fixed number of data displays, similar to those available in Excel. It’s added value, however, is that Infogram is aesthetically pleasing and it’s a browser-based tool. This means you won’t bore an audience with classic Excel charts, and it means you can access your work anywhere you have an internet connection. Check out Infogram here .

Datawrapper

Datawrapper is similar to Flourish and Infogram. The key difference is that you have a wide variety of displays to choose from like Flourish, but it requires a standard input format like Infogram.

At the end of the day, Tableau is by far the best visualization software in terms of flexibility and power. But if you’re looking for a simple, accessible solution, Flourish, Infogram and Datawrapper will do the trick. Try them out to see which is best for you!

Data Display in Excel

A quick note on data display in Excel: in addition to using the visualizations discussed above on a normal range, you can use them on a pivot table.

What are the steps to display data in a pivot table?

Imagine you have a normal range in Excel that you want to convert to a pivot table. You can do so by highlighting the range and navigating to Insert > Tables > Pivot Table. Once the field appears, drag the dimensions and measures you want into the fields.

From there, you can create a pivot table data display by placing your cursor anywhere in the pivot table and navigating to Insert and clicking a visualization. The data display is now connected to the pivot table and will change with it.

Data Display from Database

So far we’ve discussed the data display definition, types of displays, answered some common questions, compared data displays and data visualization, covered data displays with qualitative data, and explored common tools.

All of these items can be considered “front-end” topics, meaning they don’t require you to work with programming languages and underlying datasets. However, it’s worth addressing how to create a data display from a database.

At its core, a database is a storage location with 2 or more joinable tables. While IT professionals would laugh at me saying this, two tabs in Excel with a data table in each could technically be considered a database . This means that any time you create a data display or visualization using data from a structure of this nature, you’re displaying from a database.

But this would be an oversimplification !

In reality, serious databases are stored on servers accessible with SQL. Displaying data from those databases requires a tool, such as Tableau, capable of accessing those servers directly. If not, you would need to export them into Excel first, then display the data with a tool.

In short, displaying data from a database requires either a powerful visualization tool or preparatory export from the database into Excel using SQL.

What is a data display in math?

Because we’re talking about data, the numeric affiliation with math comes up often. Data displays are used in math insofar as math is used in almost every discipline. This means we don’t need to explore it extensively. However, the specific use of data displays in statistics is important.

Data Displays and Statistics

Statistics is the specific discipline of math that deals with datasets. More specifically, it deals with descriptive and inferential analytics . In short, descriptive analysis tries to understand distribution. Distribution can be broken down into central tendency and dispersion.

Inferential analysis, on the other hand, uses descriptive statistics on known data to make assumptions about a broader population. If a penny is copper (descriptive), and all pennies are the same, then all pennies are copper (inferential).

Data displays in statistics can be used for both descriptive and inferential analysis. They help the analyst understand how well their models represent the data.

Much of statistics is polluted with discipline-specific jargon, and it’s not the goal of this article to deep-dive into that world. Instead, I encourage you to get ahold of one of the data display tools we discussed and start playing with them. This is the best way to learn data display skills .

At AnalystAnswers.com, I’m working to build task-based packets to help you improve your skills. So stay tuned for those!

If you found this article helpful, check out more free content on data, finance, and business analytics at the AnalystAnswers.com homepage !

About the Author

Noah is the founder & Editor-in-Chief at AnalystAnswers. He is a transatlantic professional and entrepreneur with 5+ years of corporate finance and data analytics experience, as well as 3+ years in consumer financial products and business software. He started AnalystAnswers to provide aspiring professionals with accessible explanations of otherwise dense finance and data concepts. Noah believes everyone can benefit from an analytical mindset in growing digital world. When he's not busy at work, Noah likes to explore new European cities, exercise, and spend time with friends and family.

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Praxis Core Math

Course: praxis core math   >   unit 1, data representations | lesson.

• Data representations | Worked example
• Center and spread | Lesson
• Center and spread | Worked example
• Random sampling | Lesson
• Random sampling | Worked example
• Scatterplots | Lesson
• Scatterplots | Worked example
• Interpreting linear models | Lesson
• Interpreting linear models | Worked example
• Correlation and Causation | Lesson
• Correlation and causation | Worked example
• Probability | Lesson
• Probability | Worked example

What are data representations?

• How much of the data falls within a specified category or range of values?
• What is a typical value of the data?
• How much spread is in the data?
• Is there a trend in the data over time?
• Is there a relationship between two variables?

What skills are tested?

• Matching a data set to its graphical representation
• Matching a graphical representation to a description
• Using data representations to solve problems

How are qualitative data displayed?

• A vertical bar chart lists the categories of the qualitative variable along a horizontal axis and uses the heights of the bars on the vertical axis to show the values of the quantitative variable. A horizontal bar chart lists the categories along the vertical axis and uses the lengths of the bars on the horizontal axis to show the values of the quantitative variable. This display draws attention to how the categories rank according to the amount of data within each. Example The heights of the bars show the number of students who want to study each language. Using the bar chart, we can conclude that the greatest number of students want to study Mandarin and the least number of students want to study Latin.
• A pictograph is like a horizontal bar chart but uses pictures instead of the lengths of bars to represent the values of the quantitative variable. Each picture represents a certain quantity, and each category can have multiple pictures. Pictographs are visually interesting, but require us to use the legend to convert the number of pictures to quantitative values. Example Each represents 40 ‍   students. The number of pictures shows the number of students who want to study each language. Using the pictograph, we can conclude that twice as many students want to study French as want to study Latin.
• A circle graph (or pie chart) is a circle that is divided into as many sections as there are categories of the qualitative variable. The area of each section represents, for each category, the value of the quantitative data as a fraction of the sum of values. The fractions sum to 1 ‍   . Sometimes the section labels include both the category and the associated value or percent value for that category. Example The area of each section represents the fraction of students who want to study that language. Using the circle graph, we can conclude that just under 1 2 ‍   the students want to study Mandarin and about 1 3 ‍   want to study Spanish.

How are quantitative data displayed?

• Dotplots use one dot for each data point. The dots are plotted above their corresponding values on a number line. The number of dots above each specific value represents the count of that value. Dotplots show the value of each data point and are practical for small data sets. Example Each dot represents the typical travel time to school for one student. Using the dotplot, we can conclude that the most common travel time is 10 ‍   minutes. We can also see that the values for travel time range from 5 ‍   to 35 ‍   minutes.
• Histograms divide the horizontal axis into equal-sized intervals and use the heights of the bars to show the count or percent of data within each interval. By convention, each interval includes the lower boundary but not the upper one. Histograms show only totals for the intervals, not specific data points. Example The height of each bar represents the number of students having a typical travel time within the corresponding interval. Using the histogram, we can conclude that the most common travel time is between 10 ‍   and 15 ‍   minutes and that all typical travel times are between 5 ‍   and 40 ‍   minutes.

How are trends over time displayed?

How are relationships between variables displayed.

• (Choice A)   A
• (Choice B)   B
• (Choice C)   C
• (Choice D)   D
• (Choice E)   E
• Your answer should be
• an integer, like 6 ‍  
• a simplified proper fraction, like 3 / 5 ‍  
• a simplified improper fraction, like 7 / 4 ‍  
• a mixed number, like 1   3 / 4 ‍  
• an exact decimal, like 0.75 ‍  
• a multiple of pi, like 12   pi ‍   or 2 / 3   pi ‍  
• a proper fraction, like 1 / 2 ‍   or 6 / 10 ‍  
• an improper fraction, like 10 / 7 ‍   or 14 / 8 ‍  

Things to remember

• When matching data to a representation, check that the values are graphed accurately for all categories.
• When reporting data counts or fractions, be clear whether a question asks about data within a single category or a comparison between categories.
• When finding the number or fraction of the data meeting a criteria, watch for key words such as or , and , less than , and more than .

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Graphical Representation of Data

Graphical representation of data is an attractive method of showcasing numerical data that help in analyzing and representing quantitative data visually. A graph is a kind of a chart where data are plotted as variables across the coordinate. It became easy to analyze the extent of change of one variable based on the change of other variables. Graphical representation of data is done through different mediums such as lines, plots, diagrams, etc. Let us learn more about this interesting concept of graphical representation of data, the different types, and solve a few examples.

Definition of Graphical Representation of Data

A graphical representation is a visual representation of data statistics-based results using graphs, plots, and charts. This kind of representation is more effective in understanding and comparing data than seen in a tabular form. Graphical representation helps to qualify, sort, and present data in a method that is simple to understand for a larger audience. Graphs enable in studying the cause and effect relationship between two variables through both time series and frequency distribution. The data that is obtained from different surveying is infused into a graphical representation by the use of some symbols, such as lines on a line graph, bars on a bar chart, or slices of a pie chart. This visual representation helps in clarity, comparison, and understanding of numerical data.

Representation of Data

The word data is from the Latin word Datum, which means something given. The numerical figures collected through a survey are called data and can be represented in two forms - tabular form and visual form through graphs. Once the data is collected through constant observations, it is arranged, summarized, and classified to finally represented in the form of a graph. There are two kinds of data - quantitative and qualitative. Quantitative data is more structured, continuous, and discrete with statistical data whereas qualitative is unstructured where the data cannot be analyzed.

Principles of Graphical Representation of Data

The principles of graphical representation are algebraic. In a graph, there are two lines known as Axis or Coordinate axis. These are the X-axis and Y-axis. The horizontal axis is the X-axis and the vertical axis is the Y-axis. They are perpendicular to each other and intersect at O or point of Origin. On the right side of the Origin, the Xaxis has a positive value and on the left side, it has a negative value. In the same way, the upper side of the Origin Y-axis has a positive value where the down one is with a negative value. When -axis and y-axis intersect each other at the origin it divides the plane into four parts which are called Quadrant I, Quadrant II, Quadrant III, Quadrant IV. This form of representation is seen in a frequency distribution that is represented in four methods, namely Histogram, Smoothed frequency graph, Pie diagram or Pie chart, Cumulative or ogive frequency graph, and Frequency Polygon.

Advantages and Disadvantages of Graphical Representation of Data

Listed below are some advantages and disadvantages of using a graphical representation of data:

• It improves the way of analyzing and learning as the graphical representation makes the data easy to understand.
• It can be used in almost all fields from mathematics to physics to psychology and so on.
• It is easy to understand for its visual impacts.
• It shows the whole and huge data in an instance.
• It is mainly used in statistics to determine the mean, median, and mode for different data

The main disadvantage of graphical representation of data is that it takes a lot of effort as well as resources to find the most appropriate data and then represent it graphically.

Rules of Graphical Representation of Data

While presenting data graphically, there are certain rules that need to be followed. They are listed below:

• Suitable Title: The title of the graph should be appropriate that indicate the subject of the presentation.
• Measurement Unit: The measurement unit in the graph should be mentioned.
• Proper Scale: A proper scale needs to be chosen to represent the data accurately.
• Index: For better understanding, index the appropriate colors, shades, lines, designs in the graphs.
• Data Sources: Data should be included wherever it is necessary at the bottom of the graph.
• Simple: The construction of a graph should be easily understood.
• Neat: The graph should be visually neat in terms of size and font to read the data accurately.

Uses of Graphical Representation of Data

The main use of a graphical representation of data is understanding and identifying the trends and patterns of the data. It helps in analyzing large quantities, comparing two or more data, making predictions, and building a firm decision. The visual display of data also helps in avoiding confusion and overlapping of any information. Graphs like line graphs and bar graphs, display two or more data clearly for easy comparison. This is important in communicating our findings to others and our understanding and analysis of the data.

Types of Graphical Representation of Data

Data is represented in different types of graphs such as plots, pies, diagrams, etc. They are as follows,

Related Topics

Listed below are a few interesting topics that are related to the graphical representation of data, take a look.

• x and y graph
• Frequency Polygon
• Cumulative Frequency

Examples on Graphical Representation of Data

Example 1 : A pie chart is divided into 3 parts with the angles measuring as 2x, 8x, and 10x respectively. Find the value of x in degrees.

We know, the sum of all angles in a pie chart would give 360º as result. ⇒ 2x + 8x + 10x = 360º ⇒ 20 x = 360º ⇒ x = 360º/20 ⇒ x = 18º Therefore, the value of x is 18º.

Example 2: Ben is trying to read the plot given below. His teacher has given him stem and leaf plot worksheets. Can you help him answer the questions? i) What is the mode of the plot? ii) What is the mean of the plot? iii) Find the range.

Solution: i) Mode is the number that appears often in the data. Leaf 4 occurs twice on the plot against stem 5.

Hence, mode = 54

ii) The sum of all data values is 12 + 14 + 21 + 25 + 28 + 32 + 34 + 36 + 50 + 53 + 54 + 54 + 62 + 65 + 67 + 83 + 88 + 89 + 91 = 958

To find the mean, we have to divide the sum by the total number of values.

Mean = Sum of all data values ÷ 19 = 958 ÷ 19 = 50.42

iii) Range = the highest value - the lowest value = 91 - 12 = 79

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Practice Questions on Graphical Representation of Data

Faqs on graphical representation of data, what is graphical representation.

Graphical representation is a form of visually displaying data through various methods like graphs, diagrams, charts, and plots. It helps in sorting, visualizing, and presenting data in a clear manner through different types of graphs. Statistics mainly use graphical representation to show data.

What are the Different Types of Graphical Representation?

The different types of graphical representation of data are:

• Stem and leaf plot
• Scatter diagrams
• Frequency Distribution

Is the Graphical Representation of Numerical Data?

Yes, these graphical representations are numerical data that has been accumulated through various surveys and observations. The method of presenting these numerical data is called a chart. There are different kinds of charts such as a pie chart, bar graph, line graph, etc, that help in clearly showcasing the data.

What is the Use of Graphical Representation of Data?

Graphical representation of data is useful in clarifying, interpreting, and analyzing data plotting points and drawing line segments , surfaces, and other geometric forms or symbols.

What are the Ways to Represent Data?

Tables, charts, and graphs are all ways of representing data, and they can be used for two broad purposes. The first is to support the collection, organization, and analysis of data as part of the process of a scientific study.

What is the Objective of Graphical Representation of Data?

The main objective of representing data graphically is to display information visually that helps in understanding the information efficiently, clearly, and accurately. This is important to communicate the findings as well as analyze the data.

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12 Excellent Datasets for Data Visualization in 2022

Data Visualization Modeling posted by Elizabeth Wallace, ODSC February 3, 2022 Elizabeth Wallace, ODSC

Data visualization requires quality data just as much as any other project. Finding data visualization datasets can be frustrating, but these datasets offer excellent resources to support visualization projects of all kinds. Let’s explore the best data visualization datasets for 2022.

A Quick Word on Data Visualization

A search on Indeed revealed  over 67,000 jobs  listed just for data visualization. That doesn’t even include the general need for data scientists. Visualization skills help businesses build rapport and gain real insight from their data. 

Whether you’re a seasoned data scientist or new to the field, you can always practice visualization. These datasets offer the perfect chance to manage projects and build experience.

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FiveThirtyEight

FiveThirtyEight is a journalism site that makes its datasets from its stories available to the public. These provide researched data suitable for visualization and include sets such as airline safety, election predictions, and U.S. weather history. The sets are easily searchable, and the site continually updates.

BuzzFeed also makes data available to the public through its GitHub page. Users can find data analysis, libraries, and guides, all open source. Some example data sets include FCC comments and data breaches, fake news sites, and figure skating scores, among other varied things. Although BuzzFeed has a reputation for writing simple articles, these datasets come from investigative journalism sections.

The U.S. Census Bureau

The Census Bureau offers a wide variety of datasets on everything from population to foreign trade. These sets are free, and researchers can access them through a simple data search. The site includes maps, tables, statistics, and data profiles. These datasets span decades of information and could offer excellent infographics or other visualizations.

AWS Covid Job Impacts

For those looking for specific Covid visualization data, AWS offers this look at how Covid has impacted jobs since March 1, 2020. According to the landing page, the dataset updates daily, and researchers are free to use it under the Creative Commons license. Data comes from online job listings, and each filter segment includes the average of new job listings over a seven-day period.

Twitter Edge Nodes

This dataset allows users to build geographical representations using the 11 million nodes and 85 million edges sources in the set. It lives on Kaggle and is free for users to download and explore. Researchers can explore relationships between Twitter users, one of the biggest social media interactions available.

Earth Data offers science-related datasets for researchers in open access formats. Information comes from NASA data repositories, and users can explore everything from climate data to specific regions like oceans, to environmental challenges like wildfires. The site also includes tutorials and webinars, as well as articles. The rich data offers environmental visualizations and contains data from scientific partners as well.

Urban Atlas European Environmental Agency

Located on the Spider Portal at the United Nations site, this dataset offers spatial data on land use and land data. The data covers large urban zones with more than 100,000 inhabitants. Users can explore data through the interactive map, and data comes from sources such as web GIS or real-time monitoring. 

The GDELT Project

The Global Dataset of Events Language and Tone collects events at a global scale. It offers one of the biggest data repositories for human civilization. Researchers can explore people, locations, themes, organizations, and other types of subjects. Data is free, and users can also download RAW data sets for unique use cases. The site also offers a variety of tools as well for users with less experience doing their own visualizations.

The Open Data Institute

The Open Data Institute offers datasets covering subjects like precipitation data, electricity usage, or air quality. Researchers can explore these datasets as part of an open data project with information taken from various Italian institutions. The Node Trentino projects can offer researchers real-life utility data for visualizations and other relevant projects.

Hotel Booking Demand Data

This dataset offers the opportunity to visualize questions about travel and data. It’s best for practicing visualization to answer questions because it’s about two years old. Users can find it housed on Kaggle, and it includes booking information for a city hotel and a resort hotel, including dates, times, who stayed, and other relevant information.

The news site ProPublica makes datasets available to the public covering subjects like education, the environment, or the military. The site includes both free and premium datasets, and users can sign up for notifications of new uploaded choices. Some of the information comes from older reports and research, but the site offers valuable resources for practice or real research.

Singapore Public Data

Another civic source of data, the Singapore government makes these datasets available for research and exploration. Users can search by subject through the navigation bar or enter search terms themselves. Datasets cover subjects like the environment, education, infrastructure, and transport.

Leveraging Visualization for Data Insights

Visualization is a valuable skill for new data scientists to master. Even seasoned data scientists can always use practice to level their visualization skills. These datasets offer a range of information in a variety of subjects perfect for launching your 2022 projects.

What’s Next?

So, I bet you’re ready to upskill your AI capabilities right? Well, if you want to get the most out of AI, you’ll want to attend ODSC East this April. At ODSC East, you’ll not only expand your AI knowledge and develop unique skills, but most importantly, you’ll build up the foundation you need to help future-proof your career through upskilling with AI. Register now for 50% off all ticket types! 

Elizabeth Wallace, ODSC

Elizabeth is a Nashville-based freelance writer with a soft spot for startups. She spent 13 years teaching language in higher ed and now helps startups and other organizations explain - clearly - what it is they do. Connect with her on LinkedIn here: https://www.linkedin.com/in/elizabethawallace/

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1.3: Visual Representation of Data II - Quantitative Variables

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• Page ID 7789

• Jonathan A. Poritz
• Colorado State University – Pueblo

Now suppose we have a population and quantitative variable in which we are interested. We get a sample, which could be large or small, and look at the values of the our variable for the individuals in that sample. There are two ways we tend to make pictures of datasets like this: stem-and-leaf plots and histograms .

Stem-and-leaf Plots

One somewhat old-fashioned way to handle a modest amount of quantitative data produces something between simply a list of all the data values and a graph. It’s not a bad technique to know about in case one has to write down a dataset by hand, but very tedious – and quite unnecessary, if one uses modern electronic tools instead – if the dataset has more than a couple dozen values. The easiest case of this technique is where the data are all whole numbers in the range \(0-99\) . In that case, one can take off the tens place of each number – call it the stem – and put it on the left side of a vertical bar, and then line up all the ones places – each is a leaf – to the right of that stem. The whole thing is called a stem-and-leaf plot or, sometimes, just a stemplot .

It’s important not to skip any stems which are in the middle of the dataset, even if there are no corresponding leaves. It is also a good idea to allow repeated leaves, if there are repeated numbers in the dataset, so that the length of the row of leaves will give a good representation of how much data is in that general group of data values.

Example 1.3.1. Here is a list of the scores of 30 students on a statistics test: \[\begin{matrix} 86 & 80 & 25 & 77 & 73 & 76 & 88 & 90 & 69 & 93\\ 90 & 83 & 70 & 73 & 73 & 70 & 90 & 83 & 71 & 95\\ 40 & 58 & 68 & 69 & 100 & 78 & 87 & 25 & 92 & 74 \end{matrix}\] As we said, using the tens place (and the hundreds place as well, for the data value \(100\) ) as the stem and the ones place as the leaf, we get

[tab:stemplot1]

One nice feature stem-and-leaf plots have is that they contain all of the data values , they do not lose anything (unlike our next visualization method, for example).

[Frequency] Histograms

The most important visual representation of quantitative data is a histogram . Histograms actually look a lot like a stem-and-leaf plot, except turned on its side and with the row of numbers turned into a vertical bar, like a bar graph. The height of each of these bars would be how many

Another way of saying that is that we would be making bars whose heights were determined by how many scores were in each group of ten. Note there is still a question of into which bar a value right on the edge would count: e.g., does the data value \(50\) count in the bar to the left of that number, or the bar to the right? It doesn’t actually matter which side, but it is important to state which choice is being made.

Example 1.3.2 Continuing with the score data in Example 1.3.1 and putting all data values \(x\) satisfying \(20\le x<30\) in the first bar, values \(x\) satisfying \(30\le x<40\) in the second, values \(x\) satisfying \(40\le x<50\) in the second, etc. – that is, put data values on the edges in the bar to the right – we get the figure

Actually, there is no reason that the bars always have to be ten units wide: it is important that they are all the same size and that how they handle the edge cases (whether the left or right bar gets a data value on edge), but they could be any size. We call the successive ranges of the \(x\) coordinates which get put together for each bar the called bins or classes , and it is up to the statistician to chose whichever bins – where they start and how wide they are – shows the data best.

Typically, the smaller the bin size, the more variation (precision) can be seen in the bars ... but sometimes there is so much variation that the result seems to have a lot of random jumps up and down, like static on the radio. On the other hand, using a large bin size makes the picture smoother ... but sometimes, it is so smooth that very little information is left. Some of this is shown in the following

Example 1.3.3. Continuing with the score data in Example 1.3.1 and now using the bins with \(x\) satisfying \(10\le x<12\) , then \(12\le x<14\) , etc. , we get the histogram with bins of width 2:

If we use the bins with \(x\) satisfying \(10\le x<15\) , then \(15\le x<20\) , etc. , we get the histogram with bins of width 5:

If we use the bins with \(x\) satisfying \(20\le x<40\) , then \(40\le x<60\) , etc. , we get the histogram with bins of width 20:

Finally, if we use the bins with \(x\) satisfying \(0\le x<50\) , then \(50\le x<100\) , and then \(100\le x<150\) , we get the histogram with bins of width 50:

[Relative Frequency] Histograms

Just as we could have bar charts with absolute (§2.1) or relative (§2.2) frequencies, we can do the same for histograms. Above, in §3.2, we made absolute frequency histograms. If, instead, we divide each of the counts used to determine the heights of the bars by the total sample size, we will get fractions or percents – relative frequencies. We should then change the label on the \(y\) -axis and the tick-marks numbers on the \(y\) -axis, but otherwise the graph will look exactly the same (as it did with relative frequency bar charts compared with absolute frequency bar chars).

Example 1.3.4. Let’s make the relative frequency histogram corresponding to the absolute frequency histogram in Example 1.3.2 based on the data from Example 1.3.1 – all we have to do is change the numbers used to make heights of the bars in the graph by dividing them by the sample size, 30, and then also change the \(y\) -axis label and tick mark numbers.

How to Talk About Histograms

Histograms of course tell us what the data values are – the location along the \(x\) value of a bar is the value of the variable – and how many of them have each particular value – the height of the bar tells how many data values are in that bin. This is also given a technical name

[def:distribution] Given a variable defined on a population, or at least on a sample, the distribution of that variable is a list of all the values the variable actually takes on and how many times it takes on these values.

The reason we like the visual version of a distribution, its histogram, is that our visual intuition can then help us answer general, qualitative questions about what those data must be telling us. The first questions we usually want to answer quickly about the data are

• What is the shape of the histogram?
• Where is its center ?
• How much variability [also called spread ] does it show?

When we talk about the general shape of a histogram, we often use the terms

[def:symmskew] A histogram is symmetric if the left half is (approximately) the mirror image of the right half.

We say a histogram is skewed left if the tail on the left side is longer than on the right. In other words, left skew is when the left half of the histogram – half in the sense that the total of the bars in this left part is half of the size of the dataset – extends farther to the left than the right does to the right. Conversely, the histogram is skewed right if the right half extends farther to the right than the left does to the left.

If the shape of the histogram has one significant peak, then we say it is unimodal , while if it has several such, we say it is multimodal .

It is often easy to point to where the center of a distribution looks like it lies, but it is hard to be precise. It is particularly difficult if the histogram is “noisy,” maybe multimodal. Similarly, looking at a histogram, it is often easy to say it is “quite spread out” or “very concentrated in the center,” but it is then hard to go beyond this general sense.

Precision in our discussion of the center and spread of a dataset will only be possible in the next section, when we work with numerical measures of these features.

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2.1.1.2 - visual representations.

Frequency tables, pie charts, and bar charts can all be used to display data concerning one categorical (i.e., nominal- or ordinal-level) variable. Below are descriptions for each along with some examples. At the end of this lesson you will learn how to construct each of these using Minitab.

Frequency Tables Section  

A  frequency table  contains the counts of how often each value occurs in the dataset. Some statistical software, such as Minitab, will use the term  tally  to describe a frequency table. Frequency tables are most commonly used with nominal- and ordinal-level variables, though they may also be used with interval- or ratio-level variables if there are a limited number of possible outcomes. 

In addition to containing counts, some frequency tables may also include the percent of the dataset that falls into each category, and some may include cumulative values. A cumulative count is the number of cases in that category and all previous categories. A cumulative percent is the percent in that category and all previous categories. Cumulative counts and cumulative percentages should only be presented when the data are at least ordinal-level. 

The first example is a frequency table displaying the counts and percentages for Penn State undergraduate student enrollment by campus. Because this is a nominal-level variable, cumulative values were not included.

Penn State Fall 2019 Undergraduate Enrollments

The next example is a frequency table for an ordinal-level variable: class standing. Because ordinal-level variables have a meaningful order, we sometimes want to look at the cumulative counts or cumulative percents, which tell us the number or percent of cases at or below that level.

As an example, let's interpret the values in the "Sophomore" row. There are 22 sophomore students in this sample. There are 27 students who are sophomore or below (i.e., first-year or sophomore). In terms of percentages, 34.4% of students are sophomores and 42.2% of students are sophomores or below.

Pie Charts Section  

A pie chart displays data concerning one categorical variable by partitioning a circle into "slices" that represent the proportion in each category. When constructing a pie chart, pay special attention to the colors being used to ensure that it is accessible to individuals with different types of colorblindness. 

•   University Park (48.5%)
•   Commonwealth Campuses (34.9%)
•   PA College of Technology (6.5%)
•   World Campus (10.1%)

Bar Charts Section  

A bar chart is a graph that can be used to display data concerning one nominal- or ordinal-level variable. The bars, which may be vertical or horizontal, symbolize the number of cases in each category. Note that the bars on a bar chart are separated by spaces; this communicates that this a categorical variable. 

The first example below is a bar chart with vertical bars. The second example is a bar chart with horizontal bars. Both examples are displaying the same data. On both charts, the size of the bar represents the number of cases in that category. 

Penn State Fall 2019 Undergraduate Enrollments

Considerations Section  

Pie charts tend to work best when there are only a few categories. If a variable has many categories, a pie chart may be difficult to read. In those cases, a frequency table or bar chart may be more appropriate. Each visual display has its own strengths and weaknesses. When first starting out, you may need to make a few different types of displays to determine which most clearly communicates your data.

A Thangka cultural element classification model based on self-supervised contrastive learning and MS Triplet Attention

• Published: 25 April 2024

Cite this article

• Wenjing Tang 1 &
• Qing Xie 1 , 2  

Being a significant repository of Buddhist imagery, Thangka images are valuable historical materials of Tibetan studies, which covers many domains such as Tibetan history, politics, culture, social life and even traditional medicine and astronomy. Thangka cultural element images are the essence of Thangka images. Hence, Thangka cultural element images classification is one of the most important works of knowledge representation and mining in the field of Thangka and is the foundation of digital protection of Thangka images. However, due to the limited quantity, high complexity and the intricate textures of Thangka images, the classification of Thangka images is limited to a small number of categories and coarse granularity. Thus, a novel fusion texture feature dual-branch Thangka cultural elements classification model based on the attention mechanism and self-supervised contrastive learning has been proposed in this paper. Specifically, to address the issue of insufficient labeled samples and improve the classification performance, this method utilizes a large amount of unlabeled irrelevant data to pre-train the feature extractor through self-supervised learning. During the fine-tuning stage of the downstream task, a dual-branch feature extraction structure incorporating texture features has been designed, and MS Triplet Attention proposed by us is used for the integration of important features. Additionally, to address the problem of sample imbalance and the existence of a large number of difficult samples in the Thangka cultural element dataset, the Gradient Harmonizing Mechanism Loss has been adopted, and it has been improved by introducing a self-designed adaptive mechanism. The experimental results on Thangka cultural elements dataset prove the superiority of the proposed method over the state-of-the-art methods. The source code of our proposed algorithm and the related datasets is available at https://github.com/WiniTang/MS-BiCLR .

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Data Availability

The data to support first stage training are openly available in https://www.cs.toronto.edu . The data to support the second phase of training are available from https://github.com/WiniTang/MS-BiCLR .

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Acknowledgements

This work is supported by National College Students Innovation and Entrepreneurship Training Program, 202310497053, and National Natural Science Foundation of China, 62271360, and we appreciate the data support from Tibet Institute of Scientific and Technical Information.

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School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070, People’s Republic of China

Wenjing Tang & Qing Xie

Engineering Research Center of Intelligent Service Technology for Digital Publishing, Ministry of Education, Wuhan, China

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Conceptualization was performed by WT, QX; methodology by WT; formal analysis and investigation by WT; data curation by WT; resources by QX; programming by WT; verification by WT; visualization by WT, QX; writing—original draft preparation—by WT; writing—review and editing—by QX; funding acquisition by WT, QX; supervision by QX.

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Tang, W., Xie, Q. A Thangka cultural element classification model based on self-supervised contrastive learning and MS Triplet Attention. Vis Comput (2024). https://doi.org/10.1007/s00371-024-03397-0

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Accepted : 29 March 2024

Published : 25 April 2024

DOI : https://doi.org/10.1007/s00371-024-03397-0

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