a visual representation of worksheet data in excel

Guide To What Is The Visual Representation Of Worksheet Data

Introduction.

Visual representation of worksheet data is crucial for effectively conveying information in a way that is easy to understand and analyze. Whether for business reports, presentations, or personal use, presenting data in a visual format can enhance comprehension and decision-making . In this blog post, we will explore the importance of visual representation of worksheet data and provide a guide on how to effectively visualize data for maximum impact.

Key Takeaways

• Visual representation of worksheet data is crucial for effectively conveying information.
• Presenting data in a visual format can enhance comprehension and decision-making.
• Choosing the right visual representation for your data involves consideration of the type of data being presented and utilizing appropriate tools and software.
• Best practices for creating visual representations include simplifying complex data, ensuring accuracy and clarity, and using appropriate color schemes and labels.
• Integrating visual representations into presentations and reports can enhance data analysis and ensure consistency across different platforms.

Understanding the Visual Representation of Worksheet Data

In today's data-driven world, visual representation of worksheet data has become increasingly important for businesses and individuals to make sense of complex information. In this chapter, we will explore the definition of visual representation of data, the benefits of using visual representation, and examples of different types of visual representations.

Visual representation of data refers to the use of charts, graphs, and other visual elements to display and communicate information contained in a worksheet. It provides a visual summary of the data, making it easier for individuals to understand and analyze the information.

Visual representation offers several benefits, including:

• Enhanced understanding: Visual representations make it easier for individuals to understand complex data by presenting it in a clear and visually appealing manner.
• Quick insights: Visual representations allow for quick identification of patterns, trends, and outliers within the data.
• Effective communication: Visual representations can be used to effectively communicate data insights to others, such as stakeholders or team members.
• Improved decision-making: By presenting data visually, individuals can make more informed decisions based on a better understanding of the information at hand.

There are several types of visual representations that can be used to display worksheet data. Some common examples include:

1. Bar charts

Bar charts are used to compare different categories of data by representing them as bars of varying lengths.

2. Line graphs

Line graphs are used to show trends and changes in data over a period of time, with individual data points connected by lines.

3. Pie charts

Pie charts are used to show the proportion of each category within a dataset, with each category represented as a slice of the pie.

4. Scatter plots

Scatter plots are used to display the relationship between two variables, with individual data points represented as dots on the graph.

These are just a few examples of the many types of visual representations that can be used to effectively display worksheet data.

Choosing the Right Visual Representation for Your Data

When it comes to presenting worksheet data, choosing the right visual representation is crucial for effectively communicating insights to your audience. Whether you are creating charts, graphs, or other visual elements, it is important to consider the following factors in order to select the most appropriate representation for your data.

Consideration of the Type of Data Being Presented

Before selecting a visual representation for your data, it is essential to consider the type of data you are working with. Is the data numerical, categorical, or time-based? Understanding the nature of your data will help you determine the most suitable visual representation.

• For numerical data, options such as bar charts, line charts, and scatter plots are often effective in illustrating trends and relationships.
• Categorical data, on the other hand, may be best represented using pie charts, histograms, or stacked bar charts to show the distribution of different categories.
• Time-based data can be visually represented through line charts, area charts, or Gantt charts to display changes over time.

Determining the Most Effective Visual Representation

Once you have identified the type of data you are working with, the next step is to determine the most effective visual representation for conveying your message.

• Consider the purpose of your visualization - Are you trying to show comparisons, trends, distributions, or relationships within the data?
• Take into account the audience - What visual format will be most familiar and accessible to your target audience?
• Evaluate the complexity of the data - Some visual representations may be better suited for simplifying complex data sets, while others may be more suitable for highlighting intricate details.

Utilizing Tools and Software for Creating Visual Representations

With advancements in technology, there are various tools and software available to help create visually appealing representations of worksheet data.

• Spreadsheet software such as Microsoft Excel and Google Sheets offer a range of built-in chart and graph options, making it easy to create visual representations directly from your data.
• Data visualization tools like Tableau, Power BI, and D3.js provide more advanced features for creating interactive and dynamic visualizations.
• Graphic design software such as Adobe Illustrator and Canva allow for customizing visual elements to match your brand's aesthetic and style.

By leveraging these tools and software, you can enhance the visual appeal of your data representations and ensure they effectively convey the intended message to your audience.

Best practices for creating visual representations

When it comes to visualizing worksheet data, it's important to follow best practices to ensure that the visual representations effectively communicate the information. Here are some key guidelines to keep in mind:

One of the main goals of visual representations is to simplify complex data and make it easier for the audience to understand. When creating visuals, consider the key insights you want to convey and focus on highlighting those points. Avoid cluttering the visual with unnecessary details that may overwhelm the viewer.

Accuracy is crucial when creating visual representations of worksheet data. Double-check all data points and ensure that the visual accurately reflects the information from the worksheet. Clarity is also important – make sure that the visual is easy to read and interpret, with clear labels and appropriately sized elements.

Color can be a powerful tool for highlighting important data points and trends, but it's important to use it thoughtfully. Choose a color scheme that complements the data and consider colorblind-friendly options. Additionally, labels are essential for providing context and helping the viewer understand the visual representation. Use clear and descriptive labels to guide the audience through the data.

Common mistakes to avoid when creating visual representations

When creating visual representations of worksheet data, it's important to be mindful of common mistakes that can affect the accuracy and effectiveness of the representation. Avoiding these mistakes can help ensure that the visual representation accurately reflects the data and effectively communicates the intended message.

One common mistake when creating visual representations is over-complicating the design. It's important to remember that the visual representation should make the data easy to understand, not more difficult. Avoid using overly complex charts or graphs that may confuse the audience. Keep the design simple and focused on the key data points.

Another mistake to avoid is misinterpreting the data when creating visual representations. It's important to thoroughly understand the data being represented and ensure that the visual representation accurately reflects the underlying information. Misinterpreting the data can lead to inaccurate conclusions and decisions based on the visual representation.

One often overlooked mistake is failing to update visual representations when the underlying data changes. It's important to regularly review and update visual representations to ensure they accurately reflect the most current data. Failing to update visual representations can lead to outdated or misleading information being communicated.

Integrating visual representations into presentations and reports

Visual representations of worksheet data play a crucial role in communicating information effectively. Whether you are creating a PowerPoint presentation or a data analysis report, incorporating visual representations can significantly enhance the impact of your message.

A. Incorporating visual representations into PowerPoint slides

• Charts and graphs: Utilize various chart types such as bar, pie, and line charts to visually display data trends and comparisons.
• Infographics: Create visually appealing infographics to present complex data in a simplified and engaging manner.
• Use of images: Integrate relevant images and visuals to complement the data and enhance audience engagement.

B. Using visual representations to enhance data analysis reports

• Data visualization tools: Leverage data visualization tools to create interactive and dynamic visual representations that provide deeper insights into the data.
• Heat maps and scatter plots: Incorporate heat maps and scatter plots to showcase the distribution and correlation of data points effectively.
• Dashboard reports: Develop visually appealing dashboard reports that provide a comprehensive overview of key metrics and KPIs.

C. Ensuring consistency in visual representation across different platforms

• Standardized templates: Use standardized templates for visual representations to maintain consistency and brand coherence across various platforms.
• Color schemes and branding: Ensure that the chosen color schemes and branding elements are consistent across all visual representations to create a cohesive visual identity.
• Responsive design: Opt for responsive design principles to ensure that visual representations adapt seamlessly to different screen sizes and devices.

Visual representation of worksheet data plays a crucial role in effectively conveying information and insights. By utilizing charts, graphs, and other visual tools, users can make complex data more understandable and actionable. I encourage all readers to explore and experiment with different visual representation techniques to see how they can enhance their data analysis and decision-making processes. Let's continue to utilize visual representations effectively and drive better outcomes in our data-driven world.

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Visualizing Data in Excel: A Comprehensive Guide

By   STC

July 15, 2023

Explore the diverse data visualization possibilities in Excel that aid in analyzing and interpreting your data effectively.

Introduction

Welcome to our comprehensive guide on visualizing data in Excel. In this article, we will delve into the world of data visualization and provide you with valuable insights on how to create compelling visual representations of your data using Excel. Whether you are a beginner or an experienced Excel user, this guide will equip you with the knowledge and techniques to effectively communicate your data through visually appealing charts and graphs.

Why Data Visualization Matters

Data visualization is a powerful tool that enables us to make sense of complex datasets. It allows us to identify patterns, trends, and outliers that might not be immediately apparent in raw data. Visualizing data in Excel not only enhances our understanding of the information at hand but also enables us to communicate our findings to others in a clear and concise manner.

Getting Started with Excel Charts

• Selecting the Right Chart Type Choosing the appropriate chart type is crucial for effectively representing your data. Excel offers a wide range of chart options, including bar charts, line charts, pie charts, scatter plots, and more. Consider the nature of your data and the message you want to convey when selecting the most suitable chart type.
• Formatting and Customization Excel provides extensive formatting and customization options to refine the appearance of your charts. From adjusting axis labels to modifying colors and styles, these features allow you to create visually appealing charts that align with your brand or presentation requirements.
• Adding Data Labels and Annotations To enhance the clarity of your visualizations, Excel enables you to add data labels and annotations. These labels provide additional context and make it easier for your audience to interpret the information being presented. You can include axis labels, data point labels, and explanatory text to further enrich your charts.

Advanced-Data Visualization Techniques

• Creating PivotCharts PivotCharts are a powerful feature in Excel that allows you to visualize data from pivot tables. By summarizing and aggregating data, pivot tables provide a comprehensive overview that can be transformed into dynamic and interactive charts. Utilizing PivotCharts enables you to explore and analyze complex datasets with ease.
• Utilizing Advanced Charting Features Excel offers advanced charting features that can take your visualizations to the next level. From trendlines and error bars to 3D charts and sparklines, these tools allow you to add depth and sophistication to your data representations. Experimenting with these features can help you create visually striking charts that captivate your audience.

Best Practices for Effective Data Visualization

To ensure your data visualizations have maximum impact, keep the following best practices in mind:

• Simplify and Declutter Avoid cluttering your charts with excessive information or unnecessary embellishments. Focus on the key message you want to convey and remove any elements that distract from that message. Remember, simplicity is key when it comes to effective data visualization.
• Use Color Strategically Colors can evoke emotions and draw attention to specific areas of your charts. Use color strategically to highlight important data points or to group related information. However, be mindful of accessibility considerations and ensure that your color choices are accessible to individuals with color vision deficiencies.
• Tell a Story with Your Data Data visualization is not just about presenting numbers; it’s about telling a story. Structure your visualizations in a way that guides your audience through a narrative. Start with an introduction, present the main findings, and conclude with a clear takeaway or call to action.

In conclusion, mastering the art of visualizing data in Excel can significantly enhance your ability to analyze and communicate complex information. By selecting the right chart types, utilizing advanced techniques, and following best practices, you can create visually compelling representations that effectively convey your data’s story. We hope this comprehensive guide has provided you with the knowledge and inspiration to create outstanding data visualizations in Excel. Start exploring the power of data visualization today and unlock new insights from your data.

Check StoryTelling with Charts – The Full Story

About the author

We are passionate about the power of visual storytelling and believe that charts can convey complex information in a captivating and easily understandable way. Whether you're a data enthusiast, a business professional, or simply curious about the world around you, this page is your gateway to the world of data visualization.

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How to use Excel as a data visualization tool

Microsoft excel has more dataviz capabilities than you may realize. find out how to make your data stand out with charts, pivottables, sparklines, slicers and more..

Everyone knows Microsoft Excel as a number cruncher, but there’s a whole lot more to the popular spreadsheet program. Just as Excel can perform basic data analysis functions , it has a surprising number of data visualization tools under the hood.

“Excel isn’t explicitly a data visualization tool, it’s a spreadsheet,” says Excel developer and consultant Jon Peltier . However, one of Excel’s strengths is its flexibility, he adds. “It’s kind of a Swiss Army knife of software. In addition to formulas, Excel has structures in the worksheet that give it additional functionality for preparing data for analysis in data visualization.”

Read on for a guide to using Excel’s dataviz tools to present your data. We use the Office 365 version of Excel for Windows in the descriptions below; some features may not be available if you use a different version.

The most obvious data visualization option in Excel is its array of charts. Over the years, Microsoft has added more and more chart types to Excel, from basics like bar, line, and pie charts to more advanced styles like scatter and surface charts. Excel 2016 for Windows saw the addition of six new chart types including treemap, sunburst and waterfall, while Excel 2019 added funnel and map charts. (All the new chart types are available to Office 365 subscribers.) See “ Excel 2016 and 2019 cheat sheet ” for details about the new chart types.

A treemap chart shows categories and subcategories of data as different-sized blocks. (Click image to enlarge it.)

To add any kind of chart to a worksheet, select the data you want to chart, click the Insert tab in the Ribbon and click the Recommended Charts button. The Recommended Charts feature looks at your data and suggests a few chart types that will display that data in most impactful way. If none of its suggestions suits you, click the All Charts tab and choose the type you want. To add a trendline to the chart, click Design > Add Chart Element > Trendlines and choose a trendline style.

PivotTables and PivotCharts

A basic Excel feature from which many other visualizations derive is the PivotTable. Doing proper data visualization in Excel starts with knowing how to use PivotTables and understanding how they work, says Peltier.

PivotTables provide a summary of your data, whereby thousands of rows and dozens of columns can be packaged into an easily readable form so you can highlight information and spot trends. They also let you group your data together in different ways so you can draw conclusions more easily, such as how the bottom line for a stat would look without a certain source.

PivotTables are particularly useful if you have long rows or columns of values. Excel has a basic summarization feature, the SUM (=Sum) function, which is great if you want to total up a dozen rows and a few columns. But what if you have hundreds of rows and many columns? PivotTables are like an amped-up SUM function.

The “pivot” part of a PivotTable is the ability to rotate (or pivot) the data in the table in order to view it from a different perspective. Again, this reflects greater data manipulation than the SUM function. For example, if you wanted to look at annual sales but exclude a month or quarter, you can do it with a click or two in a PivotTable. SUM requires more manipulation.

Three PivotTables on the right sum up the raw data on the left. (Click image to enlarge it.)

In the image above, you see the original data on the left, with columns for Product, Origin, Day sold, Price, Unit Sales, and Dollar Sales. There are three PivotTables next to the columns comparing sales of each fruit from imported vs. local sources, total sales for each type of fruit, and how each fruit sold by day of the week. This type of insight is simply impossible with the SUM function.

It’s easy enough to create a PivotTable. Select the rows and columns, with the label at the top of each column, from your data set. Click the Ribbon’s Insert tab, and the first two options are PivotTable and Recommended PivotTables.

With Recommended PivotTables, Excel looks at the contents of the sheet and presents you with a list of thumbnails for various PivotTables generated from all of the categories in your spreadsheet. Select the PivotTable you want and click OK.

The alternative way requires a few more clicks but lets you build a table using your own criteria, in case the manner you’d like to sort the data isn’t among the Recommended options. Click the PivotTable button and you will be asked to select the data range, the rows and columns to be included in the table. Click and drag to cover the range you want and click OK.

Whichever method you use to create the table, Excel will open a new worksheet with the table in it. If you like, you can copy it into the original data sheet as I did in the screenshot. Once you select OK, the PivotTable Fields pane will open on the right side of the screen; here you can select the fields by which to filter your data. If you chose a Recommended PivotTable, you can tweak it by checking and unchecking the fields in the PivotTable Fields pane.

PivotTables have the side benefit of finding sloppiness in your spreadsheets. For example, I do all my personal accounting in Excel, income and expense. In generating a PivotTable for 2018 I saw I had my estimated federal taxes entered as both “Fed” and “Feds,” while deposits were listed either under the publication name or “Deposit.” This forced me to go back and clean up the entries for a more accurate view in the table.

PivotCharts are visual representations of PivotTables. To insert a PivotChart, go to the Ribbon’s Insert tab and select PivotChart. It will ask you to select the data source range, just like a PivotTable, and pick the fields to chart.

PivotCharts let you visually represent the data from a PivotTable. (Click image to enlarge it.)

Excel lets you apply filters to PivotTables to focus on a smaller portion of their data. Slicers are canned filters that slice up data by categories, letting you quickly turn on and off categories to drill down into specific information.

Slicers are inserted from an existing PivotTable, so you must make the table first. From there, go to the Insert tab and click the Slicer button. Using our fruit sales spreadsheet, you can create slicers for specificity on day, location, or unit sales. In this example, we select Product, Origin, and Day.

From there, three boxes pop up for each category. Under Product, select Apples, under Origin select Imported, and leave Day alone. Now we know the total number of imported apples sold that week and on which days. (In the example below, the days shown are automatically generated from the data. Imported apples were sold only on Wednesdays, Thursdays and Saturdays, so the slicer lists those days.)

Slicers let you quickly filter data by category, such as the product, place of origin, and day sold. (Click image to enlarge it.)

Unlike filters, slicers can work across multiple PivotTables. And that is where Slicers work best, on multiple PivotTables. For a single PivotTable, a slicer is not really different from a filter.

You connect two or more PivotTables by first creating them, then right-clicking on any of them and selecting Report Connections. This pops up a box with the connected tables. Initially, only one will be checked, the first one you made. By checking the others, you build the connection.

So again using our fruit spreadsheet, by connecting two PivotTables I can report on product and origin, and thus get a lump sum unit sales figure for locally grown fruit.

Build slicer connections between PivotTables for even more specificity. (Click image to enlarge it.)

A sparkline is a tiny basic chart that appears within a single Excel cell. You can make a line, column, or win/loss sparkline within the cell and use it to highlight trends in your data.

For example, “you might have a dashboard showing how five divisions of your company have done every quarter over the last two years,” says Peltier. If you want a visual representation of how each division has performed, he adds, “you won’t have room on the screen for five regular charts. That type of information is nicely suited to sparklines.”

Sparklines are mini-charts contained in single cells that highlight data trends, as shown at lower right. (Click image to enlarge it.)

You can create a sparkline from data in a table or a PivotTable. Select the data range, such as sales for a 12-month period, then go to the Insert tab on the Ribbon and select Line, Column or Win/Loss from the Sparklines section. Confirm the source data range, choose a destination cell for the sparkline, and click OK.

Remember that a sparkline goes in a single cell, so you’ll want to select data for it from a single row or a single column. That said, you can add multiple sparklines simultaneously by choosing a data range from multiple rows or columns, and choosing the corresponding number of destination cells for the sparklines. In other words, if you want three sparklines to show trends for three rows of data, select three destination cells.

Conditional formatting

Scanning over rows and columns of numbers in a spreadsheet can make your eyes glaze over. To make it easier to spot important data points and trends, you can use Excel’s conditional formatting feature to a table, PivotTable, or range of cells. For instance, you can tell Excel to make high values green and low values red; it will automatically lighten or darken the red and green color depending on how severe the high or low.

Three types of conditional formatting in a single cell: color, bar, and arrow. (Click image to enlarge it.)

Conditional formatting can also be used to highlight profit and loss, giving a good visualization of profit and loss over time, among regions, for different products, and so on. In addition to changing cells’ colors, you can use data bars or a set of icons.

To add conditional formatting, select the data you want to format, select the Home tab on the Ribbon and click the Conditional Formatting button in the Styles group. There you can choose which cells to highlight (e.g., greater than or less than a value, or the top or bottom 10%) and/or apply data bars, color scales or icon sets to the whole selection. The best part is you can add multiple formats. I added a light-to-dark color scale, arrows to indicate an increase or decrease from the previous month, and bars comparing the data in the cells to the single largest monthly entry.

An ancillary feature to conditional formatting and sparklines is adding tiny graphics to a spreadsheet using the Webdings font. “You can’t link to formulas but you can add pizzazz to a workbook by inserting a static picture to describe what your data is,” says Ken Puls, who runs the Excelguru Blog . This helps you summarize information so a manager can get in, see the overall trend and get out, Puls says.

The embedded icons can demonstrate things like upward and downward trends, profit and loss, or status level. To add an icon to a worksheet, go to the Insert tab on the Ribbon, select Icons in the Illustrations group, choose an icon and click Insert. The image appears on your worksheet, with “handles” so you can easily resize, rotate or move it.

Linked data types

Microsoft has recently rolled out an Excel feature it calls linked data types to Office 365 subscribers. Instead of simply numbers or strings, cells can now hold values that linked to an online data source — in this case, Microsoft’s Bing search engine.

Right now there are only two data types: stocks and geography. Excel recognizes a stock ticker symbol or state name in a cell and pulls in relevant data from Bing in real time. Cells with one of these data types include an icon indicating their status, and clicking on the icon brings up a card with relevant information.

A stock cell, for instance, can show all kinds of information about the company and its stock performance, such as current price, 52-week high and low, P/E, and more. And a geography cell can show anything from a location’s population to its power consumption.

Click a linked geography cell to see all sorts of information about that location. (Click image to enlarge it.)

Adding stock cells is quite simple. Enter in a bunch of stock tickers or company names in a single column, then highlight them and click on the Data tab in the Ribbon. In the middle of the Ribbon you’ll see a Data Types group with buttons for Stocks and Geography.

Click Stocks, and your tickers will be replaced with the names of the companies and an icon to the left of each name. Selecting a company will pop up a menu where you can select the types of data you want the company’s card to show.

Geography is also interesting. As with stocks, you enter a bunch of locations — states, countries, cities, etc. — in a single column, select the cells and click Data > Geography. You need to be precise, though. Entering GA, CA, MN and NC and then clicking the Geography button did not yield Georgia, California, Minnesota, and North Carolina for me, but Gabon, Canada, Mongolia, and New Caledonia. Geo, Calif, Minn, and N.C. did the trick. The full state names also work.

Office 365 subscribers can perform a variety of stock and location comparisons and analysis in Excel. (Click image to enlarge it.)

The array of information about locations that Bing offers is astounding, covering basics like population and GDP but also esoterics like fossil fuel consumption and life expectancy.

Excel’s new linked data types are available only to Office 365 subscribers. Microsoft says it hopes to roll out additional linked data types, but so far the company hasn’t said when.

Introduced in Excel 2016 for Windows (it’s not available for Macs), the Forecast Sheet feature uses AI to project values in the future based on past performance. It works with historical, time-based data and creates a chart with high, low, and median projections. The more data you have to work with, the more precise a prediction it makes.

Using historical data, Excel can project where your data points are headed. (Click image to enlarge it.)

To create a forecast, you must use time-based historical data. Put your cursor in one of the data cells, go to the Data tab on the Ribbon, and in the Forecast group, select Forecast Sheet. On the screen that appears, choose the type of chart (line or bar), the forecast end date, and other options. Click Create, and Excel creates a new worksheet with a chart that contains both the historical data and the predicted values, which are shaded differently than the historical data. This is considered especially helpful for seasonal data.

Quick Analysis

One last tip: If you use Excel 2016, Excel 2019, or Excel for Office 365 in Windows, you can use the Quick Analysis feature to quickly apply an array of visualizations, including conditional formatting, charts, PivotTables and sparklines, to selected data. Highlight the cells you want to analyze, and the Quick Analysis icon pops up near the lower-right corner of the highlighted data. Click the icon and choose the type of visualization you want to add.

The Quick Analysis feature gives you a variety of tools for analyzing your data instantly. (Click image to enlarge it.)

Data visualization in Excel has never been easier.

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Creating Data Visualizations in Excel: What to Keep in Mind

• 14 Oct 2021

One of the most valuable and abundant resources businesses have at their disposal is data. With vast amounts of data being generated every minute of every day, the insights gleaned can inform virtually every business decision —often resulting in favorable outcomes.

There are many data visualization tools on the market designed for creating illustrations for business purposes. Fortunately, one of the most popular and easy-to-use options is likely already installed on your computer: Microsoft Excel.

If you don’t have access to Microsoft Excel, consider using free options like Google Sheets for a similar, albeit more limited, experience.

While Excel isn’t visualization software, it’s a versatile, powerful tool for professionals of all levels who want to analyze and illustrate datasets. Here are the types of data visualizations you can create in Excel and the steps involved in doing so, along with some tips to help you along the way.

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Types of Data Visualizations in Excel

There are different data visualization techniques you can employ in Excel, depending on the data available to you and the goal you’re trying to achieve, including:

• Area charts
• Scatter plots

Other visualization techniques can be used to illustrate large or complex data sets. These include:

• Gantt charts
• Highlight tables
• Bullet graphs

More advanced visualizations, such as those that include graphic elements like geographical heat maps, may not be possible to create in Excel or require additional tools.

Related: 6 Data Visualization Examples to Inspire Your Own

How to Create Data Visualizations in Excel

The steps involved in creating data visualizations in Excel depend on the type of graph or chart you choose. For basic visualizations, the process is largely the same. More complex datasets and illustrations may require additional steps.

To craft a data visualization in Excel, start by creating an organized spreadsheet. This should include labels and your final dataset.

Then, highlight the data you wish to include in your visual, including the labels. Select “insert” from the main menu and choose the type of chart or graph you’d like to create. Once you’ve made your selection, the visualization will automatically appear in your spreadsheet.

Right-click on the chart or graph to edit details, such as the title, axes labels, and colors. Doing so will open a pop-up or side panel that includes options to add a legend, adjust the scale, and change font styles and sizes.

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Tips for Creating Visualizations in Excel

1. choose the right type of visualization.

To create an effective data visualization, it’s critical to choose the right type of chart or graph. Consider the type of data you’re using, the size of your dataset, and your intended audience.

A mismatch between the type of data being leveraged and the visual used to present it can be detrimental to viewers’ understanding of the information. Whether you’re working with qualitative or quantitative data, for example, impacts how you should display the information.

Your intended audience also influences how simplified or complex your illustration should be. For instance, when presenting to a large audience or high-level stakeholders, it’s helpful to distill your presentation to highlight key trends and insights rather than individual data points.

2. Remove Irrelevant or Inaccurate Data

Ultimately, your visualization’s quality is only as good as that of the data you use. For this reason, it’s important to clean data after it’s been collected to remove any irrelevant or inaccurate information. This process is often referred to as data wrangling or data cleaning.

Failure to thoroughly clean data prior to using it can be detrimental to its integrity and lead to inaccurate or misleading data visualizations .

Related: What Is Data Integrity & Why Does It Matter?

3. Provide Context For the Visualization

If necessary, include a key or legend and additional context to help viewers make sense of your illustration.

For example, consider a heatmap that shows the frequency of COVID-19 infections in a location over a specific period. To form a clear understanding of the information being presented, viewers need to know details such as the period being examined, the data source, and what each color means.

This context is important because it helps viewers interpret the information being displayed. Without a key clearly defining the coloring system, for instance, it would be virtually impossible to know what each color indicates, rendering the heatmap useless.

4. Tell A Story

Finally, the key to crafting a compelling visualization is to use data to tell a story. If the data illustrates a trend or supports a hypothesis, your visualization should make that clear. After all, the purpose of visualizations is to present findings in a way that’s easy for viewers to digest and understand.

Telling a story not only makes your visualization more interesting and engaging but also aids in data-driven decision-making . In addition, it helps stakeholders understand the essence of your findings and, in turn, informs their decision-making processes .

Making Data-Backed Business Decisions

Data visualization is a powerful tool when it comes to addressing business questions and making informed decisions. Learning how to create effective illustrations can empower you to share findings with key stakeholders and other audiences in a manner that’s engaging and easy to understand.

You don’t have to be in an analytics or data science role to take advantage of data visualizations. Professionals of all levels and backgrounds can develop data skills to more effectively communicate within their organizations and make informed decisions.

Are you interested in improving your analytical skills? Learn more about Business Analytics —one of the three courses that comprise CORe —which teaches you how to apply analytical techniques in Excel to solve real business problems.

About the Author

Excel Visualization: A Guide to Clear Data Presentation for Beginners

I once struggled with dull data tables.

Numbers clustered in rows and columns become a blur. But with Excel visualization , you can empower your audience to make informed decisions based on the data presented. Excel charts and graphs replace chaos, revealing patterns and trends.

Convey ideas efficiently with the right visual. It’s not just about creating a chart; it’s about making data understandable and engaging.

In this article, I’ll guide you step-by-step on transforming your Excel data into insightful visuals.

Let’s get started!

Table of Contents

Understanding the Basics of Excel Visualization

Excel provides various visualization options, whether 2D or 3D versions, standard, stacked, or 100% stacked options. It’s all about finding the right fit that best represents your data and message.

The Excel Charting Interface

Let’s start with creating a chart in Excel.

When you click on the Insert tab in Excel, you’ll see various chart types that you can use to visualize your data.

The Excel charting interface provides a wide range of options, from line and area charts to bar and column charts. When you click on a chart, the ‘ Chart Tools ’ contextual tab provides additional features for customizing your charts.

Types of Data for Visualization

Excel visualization data can be broadly categorized into numerical, categorical, and time-series data.

• Numerical data includes values that can be measured, such as sales figures or temperature readings.
• Categorical data includes information such as names, labels, or groups.
• Time-series data involves values measured over time, such as stock prices or website traffic.

Excel offers different chart types depending on your data type.

Selecting the Right Chart Type

Selecting the right chart type is half the battle for effective data visualization in Excel.

Pie charts are best for part-to-whole comparisons. Use line charts for time series or trends. Bar or column charts are the most suitable for categorical comparisons.

However, consider more advanced chart types for more complex data sets.

Scatter plots are excellent for correlation analysis , while histograms and box plots are ideal for distribution analysis of quantitative data.

It’s all about understanding your data and determining the best way to display it.

Steps for Visualizing Data in Excel – Creating Basic Charts

Creating basic charts in Excel is a fundamental skill for anyone looking to present data in a visual format.

Excel offers a variety of chart types, each with unique properties and use cases. The key to successful chart creation in Excel is understanding these different chart types and knowing how to present your data most effectively with them.

Organizing Your Data

Before you dive into creating Excel charts, it is crucial to organize your data correctly . 

Well-organized data will make the charting process easier and the resulting charts more meaningful. Ensure your data is clean, error-free, and arranged clearly and logically.

This will make it easier to select the data for your charts and create visuals that effectively communicate your data analysis results.

Pie and Donut Chart

Pie charts are popular for showing the proportion of different categories within a whole. While visually appealing, they are often misused and can lead to misleading interpretations.

Generally, they are most effective when comparing a few categories representing parts of a whole.

On the other hand, donut charts are a variation of pie charts with a hole in the middle (as the name implies!). Like pie charts, they can display multiple data series, but they should be used sparingly.

To create a pie chart in Excel:

• Select the data you want to visualize
• From the “ Insert ” tab, choose “ Pie ” from the chart options.
• You can customize your chart by changing the colors, adding labels, and adjusting other settings in the “ Format Chart Area ” pane.

Here’s a video guide on how to create a donut chart:

Line and Area Chart

Line and area charts are handy when dealing with time-series data . These charts plot data points on a graph and connect them with a line, allowing you to see trends over time. 

Check out this video for a step-by-step guide on how to create a line chart:

One of the business essentials when working with line and area charts is customizing the axis and gridlines. This can help make your chart more readable and meaningful .

The “ Format Axis ” pane allows you to customize the axis labels, adjust the scale, and add gridlines.

Column and Bar Graph

Bar and column charts are Excel’s most commonly used chart types. They are excellent for comparing different categories of data.

While bar charts and column charts are often used interchangeably, there is a difference: A bar chart presents data horizontally , while a column chart presents data vertically . This distinction can influence how easily your audience interprets the chart.

You can also choose between a stacked or clustered bar and column chart layout.

In a stacked chart , data series are stacked on each other, while in a clustered chart , they are placed side by side.

To create a bar or column chart:

• Select the data
• Then choose either “Bar” or “Column” from the chart options in the “ Insert ” tab
• Remember to format the chart and the axis labels to make the chart easier to understand

Advanced Charting Techniques

In this section, I’ll describe how to present complex data in a visually appealing and easily understandable format. Since each dataset is unique, treat these charts as ideas for meaningfully presenting your data.

Combination Charts

This type of chart combines the features of line and column charts, allowing you to present mixed data more comprehensively.

For example, when you have a target and actual data for comparison , a combination chart can be the perfect tool for visualization.

Clicking the Chart Design tab on the ribbon allows you to change the chart type and create a customized combination chart.

This allows you to have your target values in columns and the actual values marked along the line, which provides a clearer visualization of your data.

Trendlines and Data Analysis

Another essential feature of Excel charts is the ability to add trendlines. These can be linear, polynomial, or moving average trendlines.

A trendline graphically displays trends in your data , and you can extend it beyond the actual data to predict future values.

Along with trendlines, interpreting R-squared values is also crucial in data analysis. This will help you understand the relationship between your dependent and independent variables, thus enhancing your analysis results.

Check out our detailed how-to post on adding trendlines to Excel charts .

Conditional Formatting in Charts

Conditional formatting is another advanced charting technique in Excel that can enhance your data visualization. You can also add data bars, color scales, and icon sets.

These features allow you to customize your charts based on certain conditions, making it easier for your audience to understand your data. Applying these formatting options enables you to create more engaging and visually appealing charts for your data presentation.

Creating a Tornado Chart in Excel

Tornado charts are particularly effective when comparing and contrasting different variables . A well-crafted tornado chart can help you visualize how changes in several factors can impact a specific outcome – for example, the impact of inflation on NPV and IRR results.

Here’s a video showing you how to create a tornado chart:

Designing a Funnel Chart in Excel

Funnel Charts in Excel are highly effective tools for monitoring sales processes or any other process that narrows down over time.

Here are two quick methods for designing funnel charts in Excel:

Building a Waffle Chart in Excel

Waffle charts, also known as square pie or waffle bar charts, are a great way to visualize individual data points compared to the whole data set. They are a fun and engaging way to present percentages or proportions.

Here is a simple method for creating waffle charts:

Data Visualization Tips – Enhancing Chart Aesthetics

The aesthetics of your Excel chart play a significant role in how effectively your data is communicated.

A visually appealing chart is easier to understand and engages your audience. Enhancing chart aesthetics involves working with various chart elements and features, such as colors, styles, and data labels.

Adding data labels, for instance, provides additional information on your chart, making it easier to interpret.

Besides, you can customize the chart’s colors and styles to match your presentation theme or company branding.

Check out this post for more information on good dashboard design principles .

Working with Chart Elements

Working with chart elements can significantly improve the readability and effectiveness of your data visualization.

Some key chart elements you can manipulate include titles, legends, and data labels.

• Data labels provide additional context to your data and can be customized to suit your chart
• Modify axis labels and gridlines to adjust their appearance and improve readability. Check out this video on how to add gridlines to your Excel charts:

These chart elements can enhance your aesthetic appeal and make your data easier to interpret.

Customizing Chart Colors and Styles

Spicing up your Excel charts is easier than you think.

The ‘ Chart Design ‘ tab in the Excel ribbon allows you to alter your charts’ aesthetics significantly.

Navigate to the ‘ Chart Styles ‘ section, and you’ll see various styles for your chart. 

Looking for a bit more customization? No problem! Simply click the ‘ Change Colors ‘ dropdown and choose a color scheme.

You can use Excel’s preset color schemes or create a custom color palette for brand consistency. Minor visual changes can significantly affect your chart’s overall look and feel.

3D Charts and Effects

Adding a third dimension to your charts can make them pop . But be careful.

While 3D effects can add a specific wow factor, they can also lead to misinterpretations of your data if they are not used properly.

To add 3D effects to your charts, click the ‘ Chart Styles ‘ and choose a style with 3D effects. 

Remember, though, that 3D effects should be used sparingly and only when they can enhance the understanding of the data. Overuse of these effects can lead to cluttered, confusing charts. When it comes to 3D effects, less is often more .

Advanced Excel Graphics

Beyond the basic charts, Excel offers advanced graphics capabilities to take your data presentation to the next level.

This includes using Sparklines, shapes, and icons, among other features.

Sparklines are mini-charts within individual cells, each representing a row of data. They give a quick snapshot of trends, helping you understand your data at a glance.

Excel offers line, column, and win/loss types of Sparklines that you can add with the Quick Analysis tool.

Using Shapes and Icons

Remember to appropriately format these shapes and icons to convey the right message and not distract from the data.

Portraying a Story Through Data

Excel visualization is not just about creating charts or diagrams; it’s about telling a story with your data. This is where the concept of data storytelling comes in.

It’s about using visualization tools to highlight key points and trends in your data, making it easier for your audience to understand and absorb.

It’s not unlike creating a plot in a novel where rows and columns of data are the characters, and the chart is the narrative arc. Every element should convey your story effectively and compellingly, from simple bar charts to intricate trend analysis.

Exporting and Sharing Your Visualizations

Once you’ve created your data visualization in Excel, it’s important to know how to share it! This involves exporting the visual representation of data in a format that others can easily access.

Whether you’re sharing a simple bar graph or a complex infographic, the export method will depend on the intended use of the chart/graphic.

This process can be as simple as saving your chart as an image or embedding Excel visuals in PowerPoint presentations and documents.

Saving Charts as Images

One of the simplest ways to share visualizations is by saving them as images .

To do this, right-click the chart and select ‘Save as Picture.’ Several image formats are available, each with its uses.

For instance, JPEG is great for photographic images, while PNG is ideal for images with transparent backgrounds. However, it’s important to consider the resolution of your image. High resolution is crucial for clear, crisp images, especially if they’re intended for print.

Embedding Excel Visuals in Presentations and Documents

Embedding them in presentations and documents is another way to share your Excel visualizations.

This can be done in two ways: linking and embedding .

• Linking refers to connecting the original Excel file and the document where it’s inserted. Any changes made to the original file will automatically update in the document (assuming the link isn’t broken ).
• Embedding involves inserting a copy of the chart into the document. While this won’t update automatically, it ensures that the chart will always be available, regardless of the status of the original file.

Both methods have advantages and should be chosen based on your specific needs.

Frequently Asked Questions

What are some common mistakes for beginners to avoid in data visualization with excel.

Common mistakes include overcrowding the chart with too much data, using inappropriate chart types, neglecting to label axes or data points clearly, and choosing colors or styles that reduce readability.

What are the best practices for presenting Excel data visually to a non-technical audience?

Focus on simplicity and clarity .

Use straightforward chart types, avoid technical jargon, and highlight key takeaways. Ensure your charts are well-labeled, and use annotations or callouts to draw attention to important data points.

What are some resources to learn more about Excel visualization? 

For more tips and tricks, visit my YouTube channel . Alternatively, look at Chandoo’s training, where I learned many excellent dashboard design ideas.

Can Excel visualization help in career development?

Absolutely! Proficiency in Excel visualization is a valuable skill in many industries.

It’s especially relevant in fields like data science, finance, marketing, and others involving large amounts of data. Effectively communicating data through graphical representation can give you a significant advantage in your professional journey.

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MS Excel: Charts

In Microsoft Excel, a chart is often called a graph. It is a visual representation of data from a worksheet that can bring more understanding to the data than just looking at the numbers.

A chart is a powerful tool that allows you to visually display data in a variety of different chart formats such as Bar, Column, Pie, Line, Area, Doughnut, Scatter, Surface, or Radar charts. With Excel, it is easy to create a chart.

Here are some of the types of charts that you can create in Excel.

• How to create a bar chart in Excel 2016 | 2010 | 2007

Column Chart

• How to create a column chart in Excel 2016 | 2010 | 2007

• How to create a pie chart in Excel 2016 | Excel 2007

• How to create a line chart in Excel 2016 | Excel 2007

Advanced Charting

• Create a column/line chart with 8 columns and 1 line in Excel 2003
• Create a chart with two Y-axes and one shared X-axis in Excel 2007

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Excel Charting Basics: How to Make a Chart and Graph

By Joe Weller | January 22, 2018 (updated May 3, 2022)

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Organizations of all sizes and across all industries use Excel to store data. While spreadsheets are crucial for data management, they are often cumbersome and don’t provide team members with an easy-to-read view into data trends and relationships. Excel can help to transform your spreadsheet data into charts and graphs to create an intuitive overview of your data and make smart business decisions.

In this article, we’ll give you a step-by-step guide to creating a chart or graph in Excel 2016. Additionally, we’ll provide a comparison of the available chart and graph presets and when to use them, and explain related Excel functionality that you can use to build on to these simple data visualizations.

What Are Graphs and Charts in Excel?

Charts and graphs in Microsoft Excel provide a method to visualize numeric data. While both graphs and charts display sets of data points in relation to one another, charts tend to be more complex, varied, and dynamic.

People often use charts and graphs in presentations to give management, client, or team members a quick snapshot into progress or results. You can create a chart or graph to represent nearly any kind of quantitative data — doing so will save you the time and frustration of poring through spreadsheets to find relationships and trends.

It’s easy to create charts and graphs in Excel, especially since you can also store your data directly in an Excel Workbook, rather than importing data from another program. Excel also has a variety of preset chart and graph types so you can select one that best represents the data relationship(s) you want to highlight.

Tired of static spreadsheets? We were, too.

Although Microsoft Excel is familiar, you were never meant to manage work with it. See how Excel and Smartsheet compare across five factors: work management, collaboration, visibility, accessibility, and integrations.  

Watch the full comparison

When to Use Each Chart and Graph Type in Excel

Excel offers a large library of charts and graphs types to display your data. While multiple chart types might work for a given data set, you should select the chart that best fits the story that the data is telling.

In Excel 2016, there are five main categories of charts or graphs:

• Column Charts: Some of the most commonly used charts, column charts, are best used to compare information or if you have multiple categories of one variable (for example, multiple products or genres). Excel offers seven different column chart types: clustered, stacked, 100% stacked, 3-D clustered, 3-D stacked, 3-D 100% stacked, and 3-D, pictured below. Pick the visualization that will best tell your data’s story.

• Bar Charts: The main difference between bar charts and  column charts are that the bars are horizontal instead of vertical. You can often use bar charts interchangeably with column charts, although some prefer column charts when working with negative values because it is easier to visualize negatives vertically, on a y-axis.

• Pie Charts: Use pie charts to compare percentages of a whole (“whole” is the total of the values in your data). Each value is represented as a piece of the pie so you can identify the proportions. There are five pie chart types: pie, pie of pie (this breaks out one piece of the pie into another pie to show its sub-category proportions), bar of pie, 3-D pie, and doughnut.

• Line Charts:  A line chart is most useful for showing trends over time, rather than static data points. The lines connect each data point so that you can see how the value(s) increased or decreased over a period of time. The seven line chart options are line, stacked line, 100% stacked line, line with markers, stacked line with markers, 100% stacked line with markers, and 3-D line.

• Scatter Charts: Similar to line graphs, because they are useful for showing change in variables over time, scatter charts are used specifically to show how one variable affects another. (This is called correlation.) Note that bubble charts, a popular chart type, is categorized under scatter. There are seven scatter chart options: scatter, scatter with smooth lines and markers, scatter with smooth lines, scatter with straight lines and markers, scatter with straight lines, bubble, and 3-D bubble.

There are also four minor categories. These charts are more use case-specific:

• Area: Like line charts, area charts show changes in values over time. However, because the area beneath each line is solid, area charts are useful to call attention to the differences in change among multiple variables. There are six area charts: area, stacked area, 100% stacked area, 3-D area, 3-D stacked area, and 3-D 100% stacked area.

• Stock: Traditionally used to display the high, low, and closing price of stock, this type of chart is used in financial analysis and by investors. However, you can use them for any scenario if you want to display the range of a value (or the range of its predicted value) and its exact value. Choose from high-low-close, open-high-low-close, volume-high-low-close, and volume-open-high-low-close stock chart options.

• Surface: Use a surface chart to represent data across a 3-D landscape. This additional plane makes them ideal for large data sets, those with more than two variables, or those with categories within a single variable. However, surface charts can be difficult to read, so make sure your audience is familiar with them. You can choose from 3-D surface, wireframe 3-D surface, contour, and wireframe contour.

• Radar: When you want to display data from multiple variables in relation to each other use a radar chart. All variables begin from the central point. The key with radar charts is that you are comparing all individual variables in relation to each other — they are often used for comparing strengths and weaknesses of different products or employees. There are three radar chart types: radar, radar with markers, and filled radar.

Another popular chart is a waterfall chart, which is essentially a series of column graphs that show positive and negative changes over time. There is no Excel preset for a waterfall chart, but you can download a template to help make the process easier. For a full walkthrough, read How to Create a Waterfall Chart in Excel .

‌ Download Waterfall Chart Template in Excel

Top 5 Excel Chart and Graph Best Practices

Although Excel provides several layout and formatting presets to enhance the readability of your charts, you can maximize their effectiveness with other methods. Below are the top five best practices to make your charts and graphs as useful as possible:

Make It Clean: Cluttered graphs — those with excessive colors or texts — can be difficult to read and aren’t eye catching. Remove any unnecessary information so your audience can focus on the point you’re trying to get across.

Choose Appropriate Themes: Consider your audience, the topic, and the main point of your chart when selecting a theme. While it can be fun to experiment with different styles, choose the theme that best fits your purpose.  

Use Text Wisely: While charts and graphs are primarily visual tools, you will likely include some text (such as titles or axis labels). Be concise but use descriptive language, and be intentional about the orientation of any text (for example, it’s irritating to turn your head to read text written sideways on the x-axis).

Place Elements Intelligently: Pay attention to where you place titles, legends, symbols, and any other graphical elements. They should enhance your chart, not detract from it.

Sort Data Prior to Creating the Chart: People often forget to sort data or remove duplicates before creating the chart, which makes the visual unintuitive and can result in errors.

How to Chart Data in Excel

To generate a chart or graph in Excel, you must first provide the program with the data you want to display. Follow the steps below to learn how to chart data in Excel 2016.

Step 1: Enter Data into a Worksheet

• Open Excel and select New Workbook .
• Enter the data you want to use to create a graph or chart. In this example, we’re comparing the profit of five different products from 2013 to 2017. Be sure to include labels for your columns and rows. Doing so enables you to translate the data into a chart or graph with clear axis labels. You can download this sample data below.

‌ Download Column Chart Practice Data

Step 2: Select Range to Create Chart or Graph from Workbook Data

• Highlight the cells that contain the data you want to use in your graph by clicking and dragging your mouse across the cells.
• Your cell range will now be highlighted in gray and you can select a chart type.

In the following section, we’ll walk you through the specifics of creating a clustered column chart in Excel 2016.

How to Make a Chart in Excel

After you input your data and select the cell range, you’re ready to choose the chart type. In this example, we’ll create a clustered column chart from the data we used in the previous section.

Step 1: Select Chart Type

Once your data is highlighted in the Workbook, click the Insert tab on the top banner. About halfway across the toolbar is a section with several chart options. Excel provides Recommended Charts based on popularity, but you can click any of the dropdown menus to select a different template.

Step 2: Create Your Chart

• From the Insert tab, click the column chart icon and select Clustered Column .

• Excel will automatically create a clustered chart column from your selected data. The chart will appear in the center of your workbook.
• To name your chart , double click the Chart Title text in the chart and type a title. We’ll call this chart “Product Profit 2013 - 2017.”

We’ll use this chart for the rest of the walkthrough. You can download this same chart to follow along.

‌ Download Sample Column Chart Template

There are two tabs on the toolbar that you will use to make adjustments to your chart: Chart Design and Format . Excel automatically applies design, layout, and format presets to charts and graphs, but you can add customization by exploring the tabs. Next, we’ll walk you through all the available adjustments in Chart Design .

Step 3: Add Chart Elements

Adding chart elements to your chart or graph will enhance it by clarifying data or providing additional context. You can select a chart element by clicking on the Add Chart Element dropdown menu in the top left-hand corner (beneath the Home tab).

To Display or Hide Axes:

To Add Axis Titles:

To Remove or Move Chart Title:

• Click None to remove chart title.
• Click Above Chart to place the title above the chart. If you create a chart title, Excel will automatically place it above the chart.
• Click Centered Overlay to place the title within the gridlines of the chart. Be careful with this option: you don’t want the title to cover any of your data or clutter your graph (as in the example below).

To Add Data Labels:

To Add a Data Table: 

• None is the default setting, where the data table is not duplicated within the chart.

Note: If you choose to include a data table, you’ll probably want to make your chart larger to accommodate the table. Simply click the corner of your chart and use drag-and-drop to resize your chart.

To Add Error Bars:

 To Add Gridlines:

To Add a Legend:  

To Add Lines: Lines are not available for clustered column charts. However, in other chart types where you only compare two variables, you can add lines (e.g. target, average, reference, etc.) to your chart by checking the appropriate option.

To Add a Trendline:

Note: You can create separate trendlines for as many variables in your chart as you like. For example, here is our chart with trendlines for Product A and Product C. 

To Add Up/Down Bars: Up/Down Bars are not available for a column chart, but you can use them in a line chart to show increases and decreases among data points.

Step 4: Adjust Quick Layout

Step 5: Change Colors

The next dropdown menu in the toolbar is Change Colors . Click the icon and choose the color palette that fits your needs (these needs could be aesthetic, or to match your brand’s colors and style).

Step 6: Change Style

For cluster column charts, there are 14 chart styles available. Excel will default to Style 1, but you can select any of the other styles to change the chart appearance. Use the arrow on the right of the image bar to view other options.

Step 7: Switch Row/Column

In this example, switching the row and column swaps the product and year (profit remains on the y-axis). The chart is now clustered by product (not year), and the color-coded legend refers to the year (not product). To avoid confusion here, click on the legend and change the titles from Series to Years .

Step 8: Select Data

Step 9: Change Chart Type

You can also save your chart as a template by clicking Save as Template …

Step 10: Move Chart

Step 11: Change Formatting

Step 12: Delete a Chart 

To delete a chart, simply click on it and click the Delete key on your keyboard.

How to Make a Graph in Excel

Because graphs and charts serve similar functions, Excel groups all graphs under the “chart” category. To create a graph in Excel, follow the steps below.

Select Range to Create a Graph from Workbook Data

Now you have a graph. To customize your graph, you can follow the same steps explained in the previous section. All functionality for creating a chart remains the same when creating a graph.

How to Create a Table in Excel

If you don’t need to visualize your data, you can create a table in Excel instead. There are two ways to format a data set as a table: manually, or with the Format as a Table button.

• Manually: In this example, we manually added data and formatted as a table by including column and row names (products and years).
• Use Excel’s Format as Table Preset: You can also input raw data (numbers without any column and row names).

Related Excel Functionality

Excel is one of the most widely-used tools across all industries and types of organizations. Charts and graphs are great tools to visualize your work, but there are many ways to elevate your data in Excel.

We’ve created a list of additional features that allow you to do more with your data:

• Pivot Tables: A pivot table allows you to extract certain columns or rows from a data set and reorganize or summarize that subset in a report. This is useful tool if you only want to view a particular segment of a large data set, or if you want to view data from a new perspective.
• Conditional Formatting : A powerful feature that allows you to apply specific formatting to certain cells in your spreadsheet. You can use conditional formatting to highlight key pieces of information, track changes, see deadlines, and perform many other data organization functions.
• Dashboards: A powerful, visual reporting feature that pulls data from one or several datasets to display key performance indicators (KPIs), project or task status, and several other metrics. This gives the audience (team members, executives, clients, etc.) a snapshot view into project progress without surfacing private information.
• Collaborative Charts: To avoid version control issues and allow multiple team members to edit a chart simultaneously, you’ll want to use a collaborative chart tool. The desktop versions of Excel do not support this, but you can use Excel for Office 365, Microsoft’s cloud-based web application, or several other online chart tools.
• Data Series: A data series is any row or column stored in your workbook that you’ve plotted into a chart or graph. Once you’ve created your chart, you can add additional data series to it: Simply highlight the additional data you want to add and the chart will automatically update.

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• Excel Data Analysis Tutorial
• Excel Data Analysis - Home
• Data Analysis - Overview
• Data Analysis - Process
• Excel Data Analysis - Overview
• Working with Range Names
• Cleaning Data with Text Functions
• Cleaning Data Contains Date Values
• Working with Time Values
• Conditional Formatting
• Subtotals with Ranges
• Quick Analysis
• Lookup Functions
• PivotTables
• Data Visualization
• Data Validation
• Financial Analysis
• Working with Multiple Sheets
• Formula Auditing
• Advanced Data Analysis
• Advanced Data Analysis - Overview
• Data Consolidation
• What-If Analysis
• What-If Analysis with Data Tables
• What-If Analysis Scenario Manager
• What-If Analysis with Goal Seek
• Optimization with Excel Solver
• Importing Data into Excel
• Exploring Data with PivotTables
• Exploring Data with Powerpivot
• Exploring Data with Power View
• Exploring Data Power View Charts
• Exploring Data Power View Maps
• Exploring Data PowerView Multiples
• Exploring Data Power View Tiles
• Exploring Data with Hierarchies
• Aesthetic Power View Reports
• Key Performance Indicators
• Excel Data Analysis Resources
• Excel Data Analysis - Quick Guide
• Excel Data Analysis - Resources
• Excel Data Analysis - Discussion
• Selected Reading
• UPSC IAS Exams Notes
• Developer's Best Practices
• Questions and Answers
• Effective Resume Writing
• HR Interview Questions
• Computer Glossary

Excel Data Analysis - Data Visualization

You can display your data analysis reports in a number of ways in Excel. However, if your data analysis results can be visualized as charts that highlight the notable points in the data, your audience can quickly grasp what you want to project in the data. It also leaves a good impact on your presentation style.

In this chapter, you will get to know how to use Excel charts and Excel formatting features on charts that enable you to present your data analysis results with emphasis.

Visualizing Data with Charts

In Excel, charts are used to make a graphical representation of any set of data. A chart is a visual representation of the data, in which the data is represented by symbols such as bars in a Bar Chart or lines in a Line Chart. Excel provides you with many chart types and you can choose one that suits your data or you can use the Excel Recommended Charts option to view charts customized to your data and select one of those.

Refer to the Tutorial Excel Charts for more information on chart types.

In this chapter, you will understand the different techniques that you can use with the Excel charts to highlight your data analysis results more effectively.

Creating Combination Charts

Suppose you have the target and actual profits for the fiscal year 2015-2016 that you obtained from different regions.

We will create a Clustered Column Chart for these results.

As you observe, it is difficult to visualize the comparison quickly between the targets and actual in this chart. It does not give a true impact on your results.

A better way of distinguishing two types of data to compare the values is by using Combination Charts. In Excel 2013 and versions above, you can use Combo charts for the same purpose.

Use Vertical Columns for the target values and a Line with Markers for the actual values.

• Click the DESIGN tab under the CHART TOOLS tab on the Ribbon.
• Click Change Chart Type in the Type group. The Change Chart Type dialog box appears.

Click Combo.

Change the Chart Type for the series Actual to Line with Markers. The preview appears under Custom Combination.

Your Customized Combination Chart will be displayed.

As you observe in the chart, the Target values are in Columns and the Actual values are marked along the line. The data visualization has become better as it also shows you the trend of your results.

However, this type of representation does not work well when the data ranges of your two data values vary significantly.

Creating a Combo Chart with Secondary Axis

Suppose you have the data on the number of units of your product that was shipped and the actual profits for the fiscal year 2015-2016 that you obtained from different regions.

If you use the same combination chart as before, you will get the following −

In the chart, the data of No. of Units is not visible as the data ranges are varying significantly.

In such cases, you can create a combination chart with secondary axis, so that the primary axis displays one range and the secondary axis displays the other.

• Click the INSERT tab.
• Click Combo in Charts group.
• Click Create Custom Combo Chart from the drop-down list.

The Insert Chart dialog box appears with Combo highlighted.

For Chart Type, choose −

• Line with Markers for the Series No. of Units
• Clustered Column for the Series Actual Profits
• Check the Box Secondary Axis to the right of the Series No. of Units and click OK.

A preview of your chart appears under Custom Combination.

Your Combo chart appears with Secondary Axis.

You can observe the values for Actual Profits on the primary axis and the values for No. of Units on the secondary axis.

A significant observation in the above chart is for Quarter 3 where No. of Units sold is more, but the Actual Profits made are less. This could probably be assigned to the promotion costs that were incurred to increase sales. The situation is improved in Quarter 4, with a slight decrease in sales and a significant rise in the Actual Profits made.

Discriminating Series and Category Axis

Suppose you want to project the Actual Profits made in Years 2013-2016.

Create a clustered column for this data.

As you observe, the data visualization is not effective as the years are not displayed. You can overcome this by changing year to category.

Remove the header year in the data range.

Now, year is considered as a category and not a series. Your chart looks as follows −

Chart Elements and Chart Styles

Chart Elements give more descriptions to your charts, thus helping visualizing your data more meaningfully.

• Click the Chart

Three buttons appear next to the upper-right corner of the chart −

For a detailed explanation of these, refer to Excel Charts tutorial.

• Click Chart Elements.
• Click Data Labels.

• Click Chart Styles
• Select a Style and Color that suits your data.

You can use Trendline to graphically display trends in data. You can extend a Trendline in a chart beyond the actual data to predict future values.

Data Labels

Excel 2013 and later versions provide you with various options to display Data Labels. You can choose one Data Label, format it as you like, and then use Clone Current Label to copy the formatting to the rest of the Data Labels in the chart.

The Data Labels in a chart can have effects, varying shapes and sizes.

It is also possible to display the content of a cell as part of the Data Label with Insert Data Label Field.

Quick Layout

You can use Quick Layout to change the overall layout of the chart quickly by choosing one of the predefined layout options.

• Click the chart.
• Click the DESIGN tab under CHART TOOLS.
• Click Quick Layout.

Different possible layouts will be displayed. As you move on the layout options, the chart layout changes to that particular option.

Select the layout you like. The chart will be displayed with the chosen layout.

Using Pictures in Column Charts

You can create more emphasis on your data presentation by using a picture in place of columns.

Click on a Column on the Column Chart.

In the Format Data Series, click on Fill.

Select Picture.

Under Insert picture from, provide the filename or optionally clipboard if you had copied an image earlier.

The picture you have chosen will appear in place of columns in the chart.

You might have to present customer survey results of a product from different regions. Band Chart is suitable for this purpose. A Band Chart is a Line Chart with an added shaded area to display the upper and lower boundaries of groups of data.

Suppose your customer survey results from the east and west regions, month wise are −

Here, in the data < 50% is Low, 50% - 80% is Medium and > 80% is High.

With Band Chart, you can display your survey results as follows −

Create a Line Chart from your data.

Change the chart type to −

• East and West Series to Line with Markers.
• Low, Medium and High Series to Stacked Column.

Your chart looks as follows.

• Click on one of the columns.
• Change gap width to 0% in Format Data Series.

You will get Bands instead of columns.

To make the chart more presentable −

• Add Chart Title.
• Adjust Vertical Axis range.
• Change the colors of the bands to Green-Yellow-Red.
• Add Labels to bands.

The final result is the Band Chart with the defined boundaries and the survey results represented across the bands. One can quickly and clearly make out from the chart that while the survey results for the region West are satisfactory, those for the region East have a decline in the last quarter and need attention.

Thermometer Chart

When you have to represent a target value and an actual value, you can easily create a Thermometer Chart in Excel that emphatically shows these values.

With Thermometer chart, you can display your data as follows −

Arrange your data as shown below −

• Select the data.
• Create a Clustered Column chart.

As you observe, the right side Column is Target.

• Click on a Column in the chart.
• Click on Switch Row/Column on the Ribbon.

• Right click on the Target Column.
• Click on Format Data Series.
• Click on Secondary Axis.

As you observe the Primary Axis and Secondary Axis have different ranges.

• Right click the Primary Axis.
• In the Format Axis options, under Bounds, type 0 for Minimum and 1 for Maximum.
• Repeat the same for Secondary Axis.

Both Primary Axis and Secondary Axis will be set to 0% - 100%. The Target Column hides the Actual Column.

• Right click the visible column (Target)
• No fill for FILL
• Solid line for BORDER
• Blue for Color

• Axis → Primary Horizontal
• Axis → Secondary Vertical
• Chart Title
• In the chart, right click on Primary Vertical Axis
• In Format Axis options, click on TICK MARKS
• For Major type, select Inside

• Right click on the Chart Area.
• No line for BORDER

Resize the chart area, to get the shape of a thermometer.

You got your thermometer chart, with the actual value as against target value being shown. You can make this thermometer chart more impressive with some formatting.

• Insert a rectangle shape superimposing the blue rectangular part in the chart.
• Gradient fill for FILL
• Linear for Type
• 180 0 for Angle
• Set the Gradient stops at 0%, 50% and 100%.
• For the Gradient stops at 0% and 100%, choose the color black.
• For the Gradient stop at 50%, choose the color white.

• Insert an oval shape at the bottom.
• Format shape with same options.

The result is the Thermometer Chart that we started with.

Gantt Chart

A Gantt chart is a chart in which a series of horizontal lines shows the amount of work done in certain periods of time in relation to the amount of work planned for those periods.

In Excel, you can create a Gantt chart by customizing a Stacked Bar chart type so that it depicts tasks, task duration, and hierarchy. An Excel Gantt chart typically uses days as the unit of time along the horizontal axis.

Consider the following data where the column −

• Task represents the Tasks in the project
• Start represents number of days from the Start Date of the project
• Duration represents the duration of the Task

Note that Start of any Task is Start of previous Task + Duration. This is the case when the Tasks are in hierarchy.

• Create Stacked Bar Chart.

• Right-click on Start Series.
• In Format Data Series options, select No fill.

• Right-click on Categories Axis.
• In Format Axis options, select Categories in reverse order.

• Adjust the range
• Major Tick Marks at 5 day intervals
• Minor Tick Marks at 1 day intervals
• Format Data Series to make it look impressive
• Give a Chart Title

Waterfall Chart

Waterfall Chart is one of the most popular visualization tools used in small and large businesses. Waterfall charts are ideal for showing how you have arrived at a net value such as net income, by breaking down the cumulative effect of positive and negative contributions.

Excel 2016 provides Waterfall Chart type. If you are using earlier versions of Excel, you can still create a Waterfall Chart using Stacked Column Chart.

The columns are color coded so that you can quickly tell positive from negative numbers. The initial and the final value columns start on the horizontal axis, while the intermediate values are floating columns. Because of this look, Waterfall Charts are also called Bridge Charts.

Consider the following data.

Prepare the data for Waterfall Chart

Ensure the column Net Cash Flow is to the left of the Months Column (This is because you will not include this column while creating the chart)

Add 2 columns – Increase and Decrease for positive and negative cash flows respectively

Add a column Start - the first column in the chart with the start value in the Net Cash Flow

Add a column End - the last column in the chart with the end value in the Net Cash Flow

Add a column Float – that supports the intermediate columns

Compute the values for these columns as follows

In the Float column, insert a row in the beginning and at the end. Place n arbitrary value 50000. This just to have some space to the left and right of the chart

The data will be as follows.

• Select the cells C2:H18 (Exclude Net Cash Flow column)
• Create Stacked Column Chart

• Right click on the Float Series.
• Click Format Data Series.

• Right click on Negative Series.
• Select Fill Color as Red.

• Right click on Positive Series.
• Select Fill Color as Green.

• Right click on Start Series.
• Select Fill Color as Grey.
• Right click on End Series.
• Delete the Legend.

• Right click on any Series
• In Format Data Series options, select Gap Width as 10% under Series Options

Give the Chart Title. The Waterfall Chart will be displayed.

Sparklines are tiny charts placed in single cells, each representing a row of data in your selection. They provide a quick way to see trends.

You can add Sparklines with Quick Analysis tool.

• Select the data for which you want to add Sparklines.
• Keep an empty column to the right side of the data for the Sparklines.

Click SPARKLINES . The chart options displayed are based on the data and may vary.

Click Line . A Line Chart for each row is displayed in the column to the right of the data.

PivotCharts

Pivot Charts are used to graphically summarize data and explore complicated data.

A PivotChart shows Data Series, Categories, and Chart Axes the same way a standard chart does. Additionally, it also gives you interactive filtering controls right on the chart so that you can quickly analyze a subset of your data.

PivotCharts are useful when you have data in a huge PivotTable, or many complex worksheet data that includes text and numbers. A PivotChart can help you make sense of this data.

You can create a PivotChart from

• A PivotTable.
• A Data Table as a standalone without PivotTable.

PivotChart from PivotTable

To create a PivotChart follow the steps given below −

• Click the PivotTable.
• Click ANALYZE under PIVOTTABLE TOOLS on the Ribbon.
• Click on PivotChart. The Insert Chart dialog box appears.

Select Clustered Column from the option Column.

Click OK. The PivotChart is displayed.

The PivotChart has three filters – Region, Salesperson and Month.

Click the Region Filter Control option. The Search Box appears with the list of all Regions. Check boxes appear next to Regions.

Select East and South options.

The filtered data appears on both the PivotChart and the PivotTable.

PivotChart without a PivotTable

You can create a standalone PivotChart without creating a PivotTable.

• Click the Data Table.
• Click the Insert tab.
• Click PivotChart in Charts group. The Create PivotChart window appears.
• Select the Table/Range.
• Select the Location where you want the PivotChart to be placed.

You can choose a cell in the existing worksheet itself, or in a new worksheet. Click OK.

An empty PivotChart and an empty PivotTable appear along with the PivotChart Field List to build the PivotChart.

Choose the Fields to be added to the PivotChart

Arrange the Fields by dragging them into FILTERS, LEGEND (SERIES), AXIS (CATEGORIES) and VALUES

Use the Filter Controls on the PivotChart to select the Data to be placed on the PivotChart

Excel will automatically create a coupled PivotTable.

What Is Data Visualization in Excel? Elaborate in Detail

Introduction to Data Visualization  

Data visualization In Excel is a presentation technique that uses graphical methods to reveal patterns in data. The goal is to help you see the data more clearly, understand it better and make better decisions. Data visualization differs from other forms of analysis, and it usually involves creating one or more charts or graphs to convey information rather than producing raw numbers or tables.  

The visual representation of worksheet data has always been important. Still, with the growth of Big Data and its associated challenges, many people have become interested in taking another look at this form of communication.  

Many different groups use data visualization in Excel, such as  

• Researchers and statisticians  
• Business executives who need new insights about markets and customers  
• Journalists who want to present stories excitingly   
• Online marketers who want their products featured on social media platforms like Facebook or Twitter  
• Sports fans who watch games on TV screens  

Everyone benefits from good data visualization techniques!  

Excel data visualization is a way of presenting information that helps people understand and interpret data, especially when the amount of data is large or complex. Visualization can describe patterns, trends, and relationships in the data. It can also help you find outliers (data points that stand out from others) and anomalies (situations where something seems wrong).  

Data visualization increases the speed of decision-making and the success of execution by 5x for companies. On average, analytics delivers a $9.01 return on investment for every dollar spent. A CAGR of 10.2% is also expected in the global data visualization industry by 2027, reaching $19.20 billion.   

Types of Data Visualization in Excel  

Many types of data visualizations are doable in Excel. This article will focus on the main types: bar charts, line graphs, scatter plots, dashboards, and sparklines.   

Bar Charts: A bar chart is a graph showing the counts of data values in columns or rows. The bars in a bar chart usually slope downwards, indicating how much bigger the count for each category is compared to the count for the last type.   

Line Graphs: A line graph is a type of graph that shows the relationship between two sets of data points over time. Lines connect points where there is a change in value. The lines may also indicate how different groups of data are related.   

Scatter Plots: A scatter plot is a graph showing how different variables are related. A scatter plot point is usually randomly placed and plotted on a grid. Colors are then used to indicate how closely each point corresponds to particular values for the variables. Each type of data visualization can explore different aspects of data. For example, a bar chart can show how many people each city has, while a scatter plot can show how other variables are related.  

Maps : Maps are a great way to show spatial relationships between data points. For example, a map might show how many people live in each city or how much money each city spends on various types of goods.    

Dashboards: A dashboard is a data visualization showing how different data groups are related. A dashboard typically contains a set of graphs, tables, and charts. The graphs show how other data groups are related, while the tables and charts show specific information about the data. An excellent way to use a dashboard is to see how different data groups are related. For example, you might want to see how different types of customers are described or how various departments are performing.  

Sparklines: These are line graph that uses colors to indicate how different variables are related. Sparklines are good for showing how other groups of data are related or how a particular variable changes over time.  

The purpose of a SQL dashboard tool is to take users through a workflow of querying, exploring, and visualizing data by combining several different BI tools (or functions within a larger BI platform). As a result of this process, dashboards are created, which include dynamic and interactive graphs and charts meant to help you understand trends and insights from your data in a way you can understand and communicate.  

Steps to Create Data Visualization Using Excel   

This article will discuss the steps involved in creating data visualizations In Excel . We will provide an example for each step.  

Step 1: Get Your Data Into a Usable Format  

The first step is to get your data into a usable format. This is doable in several ways, but the most common way is to input it into a spreadsheet. Once your data is in a spreadsheet, you can begin making sense of it and create visualizations.  

Step 2: Create Column Charts and Bar Charts  

Column charts show information as columns and help display large amounts of data. Bar charts show information as bars and enable the display of small amounts of data. Both types of charts can be used to display trends or changes over time.  

Step 3: Create Line Graphs and Scatter Plots  

Line graphs show the relationship between two or more variables over time. Scatter plots show how different variables are related to one another. They can help you see patterns in your data that you wouldn’t otherwise be able to see.  

Step 4: Create Pie Charts and Histograms  

Pie charts show the distribution of data in categories. Histograms show the distribution of data in numerical values. They can display information such as how many people are affected by a particular event, how many different types of products are sold, or how many times a specific word is used.  

Step 5: Create Charts Using Conditional Formatting  

Charts can be made conditional by using the conditional formatting feature of Data Visualization in Excel. This allows you to make specific changes to the appearance of a chart based on certain conditions. For example, you could make a chart showing how many sales were made by day of the week or how many emails were sent out over a certain period.  

These are just a few steps in creating data visualizations in Excel. Many other techniques can be used, and the steps outlined here introduce the process. For more detailed instructions, please visit the Microsoft website or another resource that is specifically designed for data visualization.  

Tips For Creating the Best Data Visualization Using Excel  

There are a few tips for creating the best data visualization in Excel that can help make your data analysis more efficient and helpful.  

The first tip is to use formatting options to highlight specific data values quickly. For example, you can use a border or color to differentiate between different values in a column or row. This makes it easy to see which rows or columns contain information you’re interested in and makes it easier to focus on specific values when analyzing your data.  

Another option is to use charts and graphs to visualize your data. Charts allow you to display your data in a variety of ways, including by type (horizontal vs. vertical), by value (total vs. individual), and by duration (long-term vs. short-term). Graphs are handy for showing relationships between different variables, which can be especially useful for understanding complex datasets.  

Finally, don’t forget Excel’s ability to create beautiful visuals simply by formatting cells! You can create stunning visuals like flow charts and bar charts simply by adjusting the font, color, size, and alignment of cells. This allows you to create visuals that are both visually appealing and informative.  

These tips are a great way to create the best data visualization in Excel . You can easily make your data analysis more efficient and helpful by using formatting options, charts and graphs, and beautiful formatting.  

Conclusion  

In this blog post, we have explored data visualization in Excel. We have discussed the different techniques that are available and provided some examples. We have also highlighted some of Excel’s best practices for creating effective charts and graphs.  

If you want to create graphics that communicate your data effectively, Excel is a great tool to use. It has a wealth of features that make it possible to create charts and graphs that are both attractive and informative. We have started by discussing the different types of Data Visualization available in Excel. Next, we have looked at some essential elements necessary for any graph or chart: axes, labels, data points, and legends. After this, we explored some more advanced techniques, such as Data Tables and VLOOKUP/Hlookup functions. Lastly, we provided some tips for creating compelling data visualizations in Excel . To learn in detail about these Excel functions and apply the same in your everyday HR activities, People Analytics & Digital HR is the best course for you. Along with providing an IIM certification, the course helps understand and master in-depth the digital needs of new-age HR professionals.  

FAQs  

Q1. Is Excel good for Data Visualization?  

Ans. In an examination of the data or when creating charts and drawings, it has been reported that 62% of people use Excel due to its flexibility and ease of use, which enables them to explore data or create charts and drawings. The ease and flexibility of Excel make it the first port of call when it comes to creating Data Visualizations.  

Q2. What is Data Visualization in a spreadsheet?   

Ans. Using Excel as a spreadsheet tool, you can visualize numerical data in a visually appealing way so that you can better understand what is happening in the data. It is possible to organize data in a clearer and more organized spreadsheet by using graphs such as line graphs, Gantt charts, or bar graphs in addition to a spreadsheet.  

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Mastering Visual Data Representation in Excel: Unleashing the Power of 2D Filled and 3D Power Maps

Excel, a ubiquitous tool for data analysis, offers advanced features for creating visually compelling charts. If you're using Excel 2016 or a newer version, you have access to 2D Filled and 3D Power Maps, elevating your data visualization game. In this guide, we will explore how to harness the capabilities of these maps to create stunning charts based on geographic data tied to locations.

2D Filled Maps:

Let's start with 2D Filled Maps, which transform data points tied to locations into area representations rather than mere points on a map. Follow these steps to create visually appealing maps:

Prepare Your Data:

Open the worksheet "Filled Map Start," containing sales data for products in five countries. To optimize Excel's mapping capabilities, convert your data into an Excel table by selecting the cell range A1:B6 and pressing Control+T.

Access Filled Maps:

Head to the Insert tab and click on the Map icon in the Charts section. Select Filled Maps from the options.

Explore the Map:

You'll now see the map without labels. Larger sales are depicted with darker blue coloring. Right-clicking on any country allows you to access Format Data Series options, providing customization features.

Customize Your Map:

Choose from four projection options: Automatic, Mercator, Miller, and Robinson. You can control the map area, labels, and series colors. The "Show All" option ensures the entire world is displayed.

Add Finishing Touches:

Enhance your map by adding a chart title and adjusting settings. Experiment with projection, map area, and color options until you achieve the desired visual representation.

The result is a captivating 2D Filled Map showcasing sales data by country (see Figure 2).

3D Power Maps:

Excel's 3D Power Maps take geographic data visualization to the next level. Whether based on street addresses, cities, states, zip codes, countries, or latitude and longitude, you can create stunning 3D maps. Let's explore the process:

Open the worksheet "3D Map Start," containing U.S. power generation capacity information. Columns for state, county, plant capacity, and energy description will be used.

Convert Data into Table:

Select the data and convert it into an Excel table. Click on 3D Map and choose Open 3D Maps.

Configure the Layer Pane:

In the Layer pane, select the geographic data (e.g., State) as the basis for the mapped area. Choose "Capacity (Sum)" to control the height of the 3D map, representing the total generating capacity for each state.

Create a Stacked Column 3D Map:

Select "Energy Description" as the category and opt for the Stacked Column chart. This choice summarizes energy capacity for each state by category. Alternatively, you can choose from Clustered Column Map, Heat Map, Bubble Map, or Region Map.

Explore and Customize:

Your 3D Map is ready (see Figure 5). Use the +/- buttons to adjust size, arrows to move and rotate, and the mouse wheel to zoom in and out. Save your map as a Tour for playback.

Gain Insights:

Hover over columns to gain insights into each state's power sources. For example, Texas relies heavily on natural gas, Washington on hydroelectric power, and Indiana on coal.

Incorporating 2D Filled and 3D Power Maps into your Excel toolkit opens up new dimensions for visual data representation. Whether you're presenting sales data by country or unraveling the energy landscape of U.S. states, these maps provide engaging and informative charts. Experiment with the customization options and unleash the full potential of Excel's mapping features. Your data has a story to tell—let 2D Filled and 3D Power Maps help you narrate it beautifully.

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Excel Sample Data (Free Download 13 Sample Datasets)

Sample data in Excel refers to datasets used for practice purposes. While learning various Excel features and functions, or performing data analysis in Excel, users often need a sample dataset.

In this Excel tutorial, you will find 13 ideal Excel sample data. These sample datasets will cover a wide variety of areas such as sales, finance, management, sports, movies, etc. so that you can get your preferred type of data.

A set of sample data in Excel consists of multiple rows and columns. Each row represents a single observation and each column depicts a single variable. The sample data can be in xlsx or csv file format and users can download the files to get the dataset. Users can also use various Excel functions and features to load data from online sources and create their own datasets.

With meaningful sample data in Excel, users can perform various testing, learning, development, and data analysis tasks.

Here are 13 ideal sets of sample data in Excel:

Project Management Sample Data

A project management sample data is suitable for various types of data filtering, analyzing, and visualizing. Here are the variables that we have included in the sample data:

• Project Name
• Assigned to
• Days Required

Here is a preview of the project management dataset:

Download Sample Workbook

Inventory Records Sample Data in Excel

Inventory management records consist of product and stock information. In our sample dataset, we have included the following fields:

• Product Name
• Opening Stock
• Purchase/Stock-in
• Number of Units Sold
• Hand-in-stock
• Cost Price Per Unit
• Cost Price Total

Here is a preview of the inventory records sample data:

Call Center Customer Satisfaction Data

Call centers deal with customer service and receive various types of feedback from customers. In our Call Center Customer Satisfaction data, we have included the following fields:

• Customer Name
• Call Timestamp
• Response Time
• Call Duration
• Call Center

Here is a preview of our sample data:

Supermarket Sales Sample Data in Excel

Supermarket sales sample data is a popular dataset for learning and practicing your Excel skills. Here is the list of variables we have included in our supermarket sales sample data:

• Retail Price
• Order Quantity

Here is a preview of the sample supermarket sales data in Excel:

Download Practice Workbook

Employee Management Data

Employee management data contains information on all employees in an organization. In our sample employee management data in Excel, we have listed the following variables:

• Employee ID
• Designation
• Annual Salary

Here is a preview of the employee management data:

Technological Product Sample Data

Any technological product information requires various specifications. In our sample dataset, we have listed the following specifications:

• Country of Origin
• Release Date

The following image shows a preview of our sample technological product dataset:

Engineering and Manufacturing Sample Data

Engineered or manufactured products also require various specifications. Here is the list of variables we have included in our sample engineering and manufacturing sample data:

• Manufacturer
• Stock Quantity

Here is a preview of the sample dataset:

Students Marksheet Sample Data in Excel

A student mark sheet contains the student identifiers and marks in various subjects. In our sample students marksheet dataset, we have listed the following variables:

• Marks in Mathematics
• Marks in Physics
• Marks in Chemistry

Here is a preview of the sample student marksheet dataset:

2022 FIFA World Cup Performance Data

In our sample dataset, we have listed the information of each player from the World Cup-winning Argentina team. Here is the list of variables we have included:

• Player Name
• Jersey Number
• Appearances
• Goals Scored
• Assists Provided
• Dribbles per 90 Min
• Interceptions per 90 Min
• Tackles per 90 Min
• Total Duels Won per 90 Min

You can preview the sample dataset in the following image:

Tokyo Olympic Data

This sample dataset contains the team names, number of Gold, Silver, Bronze, and total medals, and ranking of teams (based on gold medal and total medal count) in the Tokyo Olympics. Here is a preview of the sample dataset:

Healthcare Insurance Sample Data in Excel

The price of healthcare insurance depends on various factors such as current age, BMI, smoking habits,  etc. In our sample healthcare insurance dataset, we have listed the following variables:

• Smoking Status
• Insurance Price

Travel Destination Distance Data in Excel

While deciding on a travel destination, we need to take the distance, available travel modes, travel duration, etc. factors into consideration. In our sample travel destination dataset, we have listed the following variables:

• Source City
• Source Latitude
• Source Longitude
• Destination City
• Destination Latitude
• Destination Longitude
• Distance (in km)
• Distance (in mile)
• Travel Mode

Netflix Movies Sample Data

The movie dataset provided in this section contains the following variables:

• IMDb Rating

You can preview the dataset in the image below:

This ends our article on Excel sample data. We provided 13 ideal sample data that you can download as an xlsx file. After downloading the workbook, you can use the dataset directly in that workbook. You can also copy or import the sample data to your target workbook using Excel functions and features. We hope that the provided datasets were helpful to you. If you have any feedback or queries on this article, feel free to share them in the comment section.

Excel Sample Data: Knowledge Hub

• GST State Code List in Excel
• Historical Data of NSE Stocks in Excel

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Tags: Learn Excel

Seemanto Saha graduated in Industrial and Production Engineering from Bangladesh University of Engineering and Technology. He has been with ExcelDemy for a year, where he wrote 40+ articles and reviewed 50+ articles. He has also worked on the ExcelDemy Forum and solved 50+ user problems. Currently, he is working as a team leader for ExcelDemy. His role is to guide his team to write reader-friendly content. His interests are Advanced Excel, Data Analysis, Charts & Dashboards, Power Query,... Read Full Bio

Thanks for the DATA SETS !

Hello Margit ,

You are most welcome.

Regards ExcelDemy

I needed Oil parameters Offshore Foundation design 2015 excel sheet access

Hello Ameer Ahmed ,

We don’t have any dataset you asked for and your question is not clear enough. Our datasets are given in the article.

Thank you so much for the data sets.

Hello Gaurav,

Where are the solutions to these problems? Would like to check against what I did

Hello Cynthia Satterwhite ,

Neither any problem nor any solution are given. These dataset are given to practice by yourself.

Wow Great Readymade Excel Sheets, Can You provide any excel sheet to maintain monthly calibration details for internal lab

Hello Karunesh Pandey ,

As calibration details varies with lab type we can provide you the criteria. You can fill out your information and edit the list.

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RECOMMENDED READS

• 5 Steps to Getting Started with Llama 2
• The Llama Ecosystem: Past, Present, and Future
• Introducing Code Llama, a state-of-the-art large language model for coding
• Meta and Microsoft Introduce the Next Generation of Llama
• Today, we’re introducing Meta Llama 3, the next generation of our state-of-the-art open source large language model.
• Llama 3 models will soon be available on AWS, Databricks, Google Cloud, Hugging Face, Kaggle, IBM WatsonX, Microsoft Azure, NVIDIA NIM, and Snowflake, and with support from hardware platforms offered by AMD, AWS, Dell, Intel, NVIDIA, and Qualcomm.
• We’re dedicated to developing Llama 3 in a responsible way, and we’re offering various resources to help others use it responsibly as well. This includes introducing new trust and safety tools with Llama Guard 2, Code Shield, and CyberSec Eval 2.
• In the coming months, we expect to introduce new capabilities, longer context windows, additional model sizes, and enhanced performance, and we’ll share the Llama 3 research paper.
• Meta AI, built with Llama 3 technology, is now one of the world’s leading AI assistants that can boost your intelligence and lighten your load—helping you learn, get things done, create content, and connect to make the most out of every moment. You can try Meta AI here .

Today, we’re excited to share the first two models of the next generation of Llama, Meta Llama 3, available for broad use. This release features pretrained and instruction-fine-tuned language models with 8B and 70B parameters that can support a broad range of use cases. This next generation of Llama demonstrates state-of-the-art performance on a wide range of industry benchmarks and offers new capabilities, including improved reasoning. We believe these are the best open source models of their class, period. In support of our longstanding open approach, we’re putting Llama 3 in the hands of the community. We want to kickstart the next wave of innovation in AI across the stack—from applications to developer tools to evals to inference optimizations and more. We can’t wait to see what you build and look forward to your feedback.

Our goals for Llama 3

With Llama 3, we set out to build the best open models that are on par with the best proprietary models available today. We wanted to address developer feedback to increase the overall helpfulness of Llama 3 and are doing so while continuing to play a leading role on responsible use and deployment of LLMs. We are embracing the open source ethos of releasing early and often to enable the community to get access to these models while they are still in development. The text-based models we are releasing today are the first in the Llama 3 collection of models. Our goal in the near future is to make Llama 3 multilingual and multimodal, have longer context, and continue to improve overall performance across core LLM capabilities such as reasoning and coding.

State-of-the-art performance

Our new 8B and 70B parameter Llama 3 models are a major leap over Llama 2 and establish a new state-of-the-art for LLM models at those scales. Thanks to improvements in pretraining and post-training, our pretrained and instruction-fine-tuned models are the best models existing today at the 8B and 70B parameter scale. Improvements in our post-training procedures substantially reduced false refusal rates, improved alignment, and increased diversity in model responses. We also saw greatly improved capabilities like reasoning, code generation, and instruction following making Llama 3 more steerable.

*Please see evaluation details for setting and parameters with which these evaluations are calculated.

In the development of Llama 3, we looked at model performance on standard benchmarks and also sought to optimize for performance for real-world scenarios. To this end, we developed a new high-quality human evaluation set. This evaluation set contains 1,800 prompts that cover 12 key use cases: asking for advice, brainstorming, classification, closed question answering, coding, creative writing, extraction, inhabiting a character/persona, open question answering, reasoning, rewriting, and summarization. To prevent accidental overfitting of our models on this evaluation set, even our own modeling teams do not have access to it. The chart below shows aggregated results of our human evaluations across of these categories and prompts against Claude Sonnet, Mistral Medium, and GPT-3.5.

Preference rankings by human annotators based on this evaluation set highlight the strong performance of our 70B instruction-following model compared to competing models of comparable size in real-world scenarios.

Our pretrained model also establishes a new state-of-the-art for LLM models at those scales.

To develop a great language model, we believe it’s important to innovate, scale, and optimize for simplicity. We adopted this design philosophy throughout the Llama 3 project with a focus on four key ingredients: the model architecture, the pretraining data, scaling up pretraining, and instruction fine-tuning.

Model architecture

In line with our design philosophy, we opted for a relatively standard decoder-only transformer architecture in Llama 3. Compared to Llama 2, we made several key improvements. Llama 3 uses a tokenizer with a vocabulary of 128K tokens that encodes language much more efficiently, which leads to substantially improved model performance. To improve the inference efficiency of Llama 3 models, we’ve adopted grouped query attention (GQA) across both the 8B and 70B sizes. We trained the models on sequences of 8,192 tokens, using a mask to ensure self-attention does not cross document boundaries.

Training data

To train the best language model, the curation of a large, high-quality training dataset is paramount. In line with our design principles, we invested heavily in pretraining data. Llama 3 is pretrained on over 15T tokens that were all collected from publicly available sources. Our training dataset is seven times larger than that used for Llama 2, and it includes four times more code. To prepare for upcoming multilingual use cases, over 5% of the Llama 3 pretraining dataset consists of high-quality non-English data that covers over 30 languages. However, we do not expect the same level of performance in these languages as in English.

To ensure Llama 3 is trained on data of the highest quality, we developed a series of data-filtering pipelines. These pipelines include using heuristic filters, NSFW filters, semantic deduplication approaches, and text classifiers to predict data quality. We found that previous generations of Llama are surprisingly good at identifying high-quality data, hence we used Llama 2 to generate the training data for the text-quality classifiers that are powering Llama 3.

We also performed extensive experiments to evaluate the best ways of mixing data from different sources in our final pretraining dataset. These experiments enabled us to select a data mix that ensures that Llama 3 performs well across use cases including trivia questions, STEM, coding, historical knowledge, etc.

Scaling up pretraining

To effectively leverage our pretraining data in Llama 3 models, we put substantial effort into scaling up pretraining. Specifically, we have developed a series of detailed scaling laws for downstream benchmark evaluations. These scaling laws enable us to select an optimal data mix and to make informed decisions on how to best use our training compute. Importantly, scaling laws allow us to predict the performance of our largest models on key tasks (for example, code generation as evaluated on the HumanEval benchmark—see above) before we actually train the models. This helps us ensure strong performance of our final models across a variety of use cases and capabilities.

We made several new observations on scaling behavior during the development of Llama 3. For example, while the Chinchilla-optimal amount of training compute for an 8B parameter model corresponds to ~200B tokens, we found that model performance continues to improve even after the model is trained on two orders of magnitude more data. Both our 8B and 70B parameter models continued to improve log-linearly after we trained them on up to 15T tokens. Larger models can match the performance of these smaller models with less training compute, but smaller models are generally preferred because they are much more efficient during inference.

To train our largest Llama 3 models, we combined three types of parallelization: data parallelization, model parallelization, and pipeline parallelization. Our most efficient implementation achieves a compute utilization of over 400 TFLOPS per GPU when trained on 16K GPUs simultaneously. We performed training runs on two custom-built 24K GPU clusters . To maximize GPU uptime, we developed an advanced new training stack that automates error detection, handling, and maintenance. We also greatly improved our hardware reliability and detection mechanisms for silent data corruption, and we developed new scalable storage systems that reduce overheads of checkpointing and rollback. Those improvements resulted in an overall effective training time of more than 95%. Combined, these improvements increased the efficiency of Llama 3 training by ~three times compared to Llama 2.

Instruction fine-tuning

To fully unlock the potential of our pretrained models in chat use cases, we innovated on our approach to instruction-tuning as well. Our approach to post-training is a combination of supervised fine-tuning (SFT), rejection sampling, proximal policy optimization (PPO), and direct preference optimization (DPO). The quality of the prompts that are used in SFT and the preference rankings that are used in PPO and DPO has an outsized influence on the performance of aligned models. Some of our biggest improvements in model quality came from carefully curating this data and performing multiple rounds of quality assurance on annotations provided by human annotators.

Learning from preference rankings via PPO and DPO also greatly improved the performance of Llama 3 on reasoning and coding tasks. We found that if you ask a model a reasoning question that it struggles to answer, the model will sometimes produce the right reasoning trace: The model knows how to produce the right answer, but it does not know how to select it. Training on preference rankings enables the model to learn how to select it.

Building with Llama 3

Our vision is to enable developers to customize Llama 3 to support relevant use cases and to make it easier to adopt best practices and improve the open ecosystem. With this release, we’re providing new trust and safety tools including updated components with both Llama Guard 2 and Cybersec Eval 2, and the introduction of Code Shield—an inference time guardrail for filtering insecure code produced by LLMs.

We’ve also co-developed Llama 3 with torchtune , the new PyTorch-native library for easily authoring, fine-tuning, and experimenting with LLMs. torchtune provides memory efficient and hackable training recipes written entirely in PyTorch. The library is integrated with popular platforms such as Hugging Face, Weights & Biases, and EleutherAI and even supports Executorch for enabling efficient inference to be run on a wide variety of mobile and edge devices. For everything from prompt engineering to using Llama 3 with LangChain we have a comprehensive getting started guide and takes you from downloading Llama 3 all the way to deployment at scale within your generative AI application.

A system-level approach to responsibility

We have designed Llama 3 models to be maximally helpful while ensuring an industry leading approach to responsibly deploying them. To achieve this, we have adopted a new, system-level approach to the responsible development and deployment of Llama. We envision Llama models as part of a broader system that puts the developer in the driver’s seat. Llama models will serve as a foundational piece of a system that developers design with their unique end goals in mind.

Instruction fine-tuning also plays a major role in ensuring the safety of our models. Our instruction-fine-tuned models have been red-teamed (tested) for safety through internal and external efforts. ​​Our red teaming approach leverages human experts and automation methods to generate adversarial prompts that try to elicit problematic responses. For instance, we apply comprehensive testing to assess risks of misuse related to Chemical, Biological, Cyber Security, and other risk areas. All of these efforts are iterative and used to inform safety fine-tuning of the models being released. You can read more about our efforts in the model card .

Llama Guard models are meant to be a foundation for prompt and response safety and can easily be fine-tuned to create a new taxonomy depending on application needs. As a starting point, the new Llama Guard 2 uses the recently announced MLCommons taxonomy, in an effort to support the emergence of industry standards in this important area. Additionally, CyberSecEval 2 expands on its predecessor by adding measures of an LLM’s propensity to allow for abuse of its code interpreter, offensive cybersecurity capabilities, and susceptibility to prompt injection attacks (learn more in our technical paper ). Finally, we’re introducing Code Shield which adds support for inference-time filtering of insecure code produced by LLMs. This offers mitigation of risks around insecure code suggestions, code interpreter abuse prevention, and secure command execution.

With the speed at which the generative AI space is moving, we believe an open approach is an important way to bring the ecosystem together and mitigate these potential harms. As part of that, we’re updating our Responsible Use Guide (RUG) that provides a comprehensive guide to responsible development with LLMs. As we outlined in the RUG, we recommend that all inputs and outputs be checked and filtered in accordance with content guidelines appropriate to the application. Additionally, many cloud service providers offer content moderation APIs and other tools for responsible deployment, and we encourage developers to also consider using these options.

Deploying Llama 3 at scale

Llama 3 will soon be available on all major platforms including cloud providers, model API providers, and much more. Llama 3 will be everywhere .

Our benchmarks show the tokenizer offers improved token efficiency, yielding up to 15% fewer tokens compared to Llama 2. Also, Group Query Attention (GQA) now has been added to Llama 3 8B as well. As a result, we observed that despite the model having 1B more parameters compared to Llama 2 7B, the improved tokenizer efficiency and GQA contribute to maintaining the inference efficiency on par with Llama 2 7B.

For examples of how to leverage all of these capabilities, check out Llama Recipes which contains all of our open source code that can be leveraged for everything from fine-tuning to deployment to model evaluation.

What’s next for Llama 3?

The Llama 3 8B and 70B models mark the beginning of what we plan to release for Llama 3. And there’s a lot more to come.

Our largest models are over 400B parameters and, while these models are still training, our team is excited about how they’re trending. Over the coming months, we’ll release multiple models with new capabilities including multimodality, the ability to converse in multiple languages, a much longer context window, and stronger overall capabilities. We will also publish a detailed research paper once we are done training Llama 3.

To give you a sneak preview for where these models are today as they continue training, we thought we could share some snapshots of how our largest LLM model is trending. Please note that this data is based on an early checkpoint of Llama 3 that is still training and these capabilities are not supported as part of the models released today.

We’re committed to the continued growth and development of an open AI ecosystem for releasing our models responsibly. We have long believed that openness leads to better, safer products, faster innovation, and a healthier overall market. This is good for Meta, and it is good for society. We’re taking a community-first approach with Llama 3, and starting today, these models are available on the leading cloud, hosting, and hardware platforms with many more to come.

Try Meta Llama 3 today

We’ve integrated our latest models into Meta AI, which we believe is the world’s leading AI assistant. It’s now built with Llama 3 technology and it’s available in more countries across our apps.

You can use Meta AI on Facebook, Instagram, WhatsApp, Messenger, and the web to get things done, learn, create, and connect with the things that matter to you. You can read more about the Meta AI experience here .

Visit the Llama 3 website to download the models and reference the Getting Started Guide for the latest list of all available platforms.

You’ll also soon be able to test multimodal Meta AI on our Ray-Ban Meta smart glasses.

As always, we look forward to seeing all the amazing products and experiences you will build with Meta Llama 3.

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