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מה יוכלו התלמידים לעשות?

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מה יוכלו התלמידים לעשות?

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בסיום השיעור, התלמידים יוכלו ליצור ולנתח ויזואליזציות של נתונים כדי להעביר מידע בצורה ברורה ויזואלית.

# data[].attributes.body
איך ויזואליזציה של מידע מקִלה על תהליך קליטת המידע?

# data[].attributes.body
הסבר על חשיבות ויזואליזציה של נתונים. הצגת דוגמאות שונות כגון גרפים, דיאגרמות ומפות חום.

# data[].attributes.body
למידה בקבוצות על יצירת גרף באקסל ושיתוף בפדלט לצורך משוב.

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Charts help you visualize your data in a way that creates maximum impact on your audience. Learn to create a chart and add a trendline. You can start your document from a recommended chart or choose one from our collection of pre-built chart templates.

Create a chart

1. Select data for the chart.

2. Select Insert > Recommended Charts.

3. Select a chart on the Recommended Charts tab, to preview the chart.

Note: You can select the data you want in the chart and press ALT + F1 to create a chart immediately, but it might not be the best chart for the data. If you don't see a chart you like, select the All Charts tab to see all chart types.

4. Select a chart.

Add a trendline

1. Select a chart.

2. Select Design > Add Chart Element.

3. Select Trendline and then select the type of trendline you want, such as Linear, Exponential, Linear Forecast, or Moving Average.

Note: Some of the content in this topic may not be applicable to some languages.

Charts display data in a graphical format that can help you and your audience visualize relationships between data. When you create a chart, you can select from many chart types (for example, a stacked column chart or a 3-D exploded pie chart). After you create a chart, you can customize it by applying chart quick layouts or styles.

Charts contain several elements, such as a title, axis labels, a legend, and gridlines. You can hide or display these elements, and you can also change their location and formatting.

Chart title

Plot area

Legend

Axis titles

Axis labels

Tick marks

Gridlines

You can create a chart in Excel, Word, and PowerPoint. However, the chart data is entered and saved in an Excel worksheet. If you insert a chart in Word or PowerPoint, a new sheet is opened in Excel. When you save a Word document or PowerPoint presentation that contains a chart, the chart's underlying Excel data is automatically saved within the Word document or PowerPoint presentation.

Note: The Excel Workbook Gallery replaces the former Chart Wizard. By default, the Excel Workbook Gallery opens when you open Excel. From the gallery, you can browse templates and create a new workbook based on one of them. If you don't see the Excel Workbook Gallery, on the File menu, click New from Template.

1. Click the Insert tab, and then click the arrow next to Chart.

2. Click a chart type, and then double-click the chart you want to add.

When you insert a chart into Word or PowerPoint, an Excel worksheet opens that contains a table of sample data.

3. In Excel, replace the sample data with the data that you want to plot in the chart. If you already have your data in another table, you can copy the data from that table and then paste it over the sample data. See the following table for guidelines for how to arrange the data to fit your chart type.

For this chart type

Arrange the data

Area, bar, column, doughnut, line, radar, or surface chart

In columns or rows, as in the following examples:

or

Bubble chart

In columns, putting x values in the first column and corresponding y values and bubble size values in adjacent columns, as in the following examples:

X-Values

Y-Value 1

Size 1

Pie chart

In one column or row of data and one column or row of data labels, as in the following examples:

or

1st Qtr

2nd Qtr

3rd Qtr

Sales

25

30

45

Stock chart

In columns or rows in the following order, using names or dates as labels, as in the following examples:

Open

High

Low

Close

1/5/02

44

55

11

25

1/6/02

25

57

12

38

or

X Y (scatter) chart

In columns, putting x values in the first column and corresponding y values in adjacent columns, as in the following examples:

or

X-Values

0.7

1.8

2.6

Y-Value 1

2.7

3.2

0.08

4. To change the number of rows and columns included in the chart, rest the pointer on the lower-right corner of the selected data, and then drag to select additional data. In the following example, the table is expanded to include additional categories and data series.

5. To see the results of your changes, switch back to Word or PowerPoint.

Note: When you close the Word document or the PowerPoint presentation that contains the chart, the chart's Excel data table closes automatically.

After you create a chart, you might want to change the way that table rows and columns are plotted in the chart. For example, your first version of a chart might plot the rows of data from the table on the chart's vertical (value) axis, and the columns of data on the horizontal (category) axis. In the following example, the chart emphasizes sales by instrument.

However, if you want the chart to emphasize the sales by month, you can reverse the way the chart is plotted.

1. Click the chart.

2. Click the Chart Design tab, and then click Switch Row/Column.

If Switch Row/Column is not available

Switch Row/Column is available only when the chart's Excel data table is open and only for certain chart types. You can also edit the data by clicking the chart, and then editing the worksheet in Excel.

1. Click the chart.

2. Click the Chart Design tab, and then click Quick Layout.

3. Click the layout you want.

To immediately undo a quick layout that you applied, press + Z .

Chart styles are a set of complementary colors and effects that you can apply to your chart. When you select a chart style, your changes affect the whole chart.

1. Click the chart.

2. Click the Chart Design tab, and then click the style you want.

To see more styles, point to a style, and then click .

To immediately undo a style that you applied, press + Z .

1. Click the chart, and then click the Chart Design tab.

2. Click Add Chart Element.

See also

Update the data in an existing chart

Chart types

Create a chart

You can create a chart for your data in Excel for the web. Depending on the data you have, you can create a column, line, pie, bar, area, scatter, or radar chart.

1. Click anywhere in the data for which you want to create a chart.

To plot specific data into a chart, you can also select the data.

2. Select Insert > Charts > and the chart type you want.

3. On the menu that opens, select the option you want. Hover over a chart to learn more about it.

Tip: Your choice isn't applied until you pick an option from a Charts command menu. Consider reviewing several chart types: as you point to menu items, summaries appear next to them to help you decide.

4. To edit the chart (titles, legends, data labels), select the Chart tab and then select Format.

5. In the Chart pane, adjust the setting as needed. You can customize settings for the chart's title, legend, axis titles, series titles, and more.

Available chart types

It's a good idea to review your data and decide what type of chart would work best. The available types are listed below.

Data that is arranged in one column or row on a worksheet can be plotted in a pie chart. Pie charts show the size of items in one data series, proportional to the sum of the items. The data points in a pie chart are shown as a percentage of the whole pie.

Consider using a pie chart when:

You have only one data series.

None of the values in your data are negative.

Almost none of the values in your data are zero values.

You have no more than seven categories, all of which represent parts of the whole pie.

Data that is arranged in columns or rows only on a worksheet can be plotted in a doughnut chart. Like a pie chart, a doughnut chart shows the relationship of parts to a whole, but it can contain more than one data series.

Tip: Doughnut charts are not easy to read. You may want to use a stacked column or stacked bar chart instead.

Data that is arranged in columns or rows on a worksheet can be plotted in a bar chart. Bar charts illustrate comparisons among individual items. In a bar chart, the categories are typically organized along the vertical axis, and the values along the horizontal axis.

Consider using a bar chart when:

Types of bar charts

Data that is arranged in columns and rows on a worksheet can be plotted in an scatter chart. Place the x values in one row or column, and then enter the corresponding y values in the adjacent rows or columns.

A scatter chart has two value axes: a horizontal (x) and a vertical (y) value axis. It combines x and y values into single data points and shows them in irregular intervals, or clusters. Scatter charts are typically used for showing and comparing numeric values, like scientific, statistical, and engineering data.

Consider using a scatter chart when:

You want to change the scale of the horizontal axis.

You want to make that axis a logarithmic scale.

Values for horizontal axis are not evenly spaced.

There are many data points on the horizontal axis.

You want to adjust the independent axis scales of a scatter chart to reveal more information about data that includes pairs or grouped sets of values.

You want to show similarities between large sets of data instead of differences between data points.

You want to compare many data points without regard to time - the more data that you include in a scatter chart, the better the comparisons you can make.

Types of scatter charts

Add or edit a chart title

You can add or edit a chart title, customize its look, and include it on the chart.

1. Click Format to open the chart formatting options.

2. In the Chart pane, expand the Chart Title section.

3. Use the switch to hide the title if you don't want your chart to show a title.

Add axis titles to improve chart readability

Adding titles to the horizontal and vertical axes in charts that have axes can make them easier to read. You can't add axis titles to charts that don't have axes, such as pie and doughnut charts.

Much like chart titles, axis titles help the people who view the chart understand what the data is about.

1. Click Format to open the chart formatting options.

2. In the Chart pane, expand the Horizontal Axis or Vertical Axis section.

3. Add or edit the Horizontal Axis or Vertical Axis options to meet your needs.

4. Expand the Axis Title.

5. Use the switch to show or hide the title.

Change the axis labels

Axis labels are shown below the horizontal axis and next to the vertical axis. Your chart uses text in the source data for these axis labels.

To change the text of the category labels on the horizontal or vertical axis:

1. Click the cell which has the label text you want to change.

2. Type the text you want and press Enter.

The axis labels in the chart are automatically updated with the new text.

Tip: Axis labels are different from axis titles you can add to describe what is shown on the axes. Axis titles aren't automatically shown in a chart.

Remove the axis labels

To remove labels on the horizontal or vertical axis:

1. Click Format to open the chart formatting options.

2. In the Chart pane, expand the Horizontal Axis or Vertical Axis section.

3. From the dropdown box for Label Position, select None to prevent the labels from showing on the chart.

Need more help?

You can always ask an expert in the Excel Tech Community or get support in Communities.

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Learn how to create a chart in Excel and add a trendline. Visualize your data with a column, bar, pie, line, or scatter chart (or graph) in Office.

# data[].attributes.body
תרגול יצירת דיאגרמה על נושא לפי בחירת התלמידים. ניתן להשתמש בפדלט להצגת העבודה.

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According to a Fortune Business Insights report, the data visualization market in 2019 was estimated at $8.85 billion. By 2027, the market worth is expected to be $19.20 billion at a compound annual growth rate of 10.2%.

The proliferation of smartphones, growing Internet use, rapid advancements in Machine Learning, and the rising adoption of cloud computing technologies as well as the Internet of Things are driving the global data visualization market.

In addition, the increasing inclination for smart factories and the ever-widening use of visual analytics, information visualization, and scientific visualization in both small and large organizations are also contributing to the data visualization market growth.

What Is Data Visualization?

Data visualization is the process of graphical representation of data in the form of geographic maps, charts, sparklines, infographics, heat maps, or statistical graphs.

Data presented through visual elements is easy to understand and analyze, enabling the effective extraction of actionable insights from the data. Relevant stakeholders can then use the findings to make more efficient real-time decisions.

Data visualization tools, incorporating support for streaming data, AI integration, embeddability, collaboration, interactive exploration, and self-service capabilities, facilitate the visual representation of data.

Here are 15 top-notch data visualization tools that are gaining market recognition for their impressive performance and usability.

What Are Data Visualization Tools?

Data is becoming increasingly important every day. For any organisation, you can understand how important data is while making crucial decisions. For the same reason, data visualisation is grabbing people's attention. Modern data visualisation tools and advanced software are on the market. A data visualisation tool is a software that is used to visualise data. The features of each tool vary, but at their most basic, they allow you to input a dataset and graphically alter it. Most, but not all, come with pre-built templates for creating simple visualisations.

What Do the Best Data Visualization Tools Have in Common?

All of the technologies available on the market for data visualisation have something or another feature in common. The first advantage is their simplicity of usage. There are two types of software that you will most likely encounter: those that are easy to use and those that are really difficult to visualise data. Some include good documentation and tutorials and are constructed in user-friendly ways. Others, regardless of their other qualities, are missing in certain areas, excluding them from any list of "best" tools. The one thing you should ensure is that the software can handle large amounts of data and many kinds of data in a single display.

The better software can also generate a variety of charts, graphs, and maps kinds. Obviously, there will be others in the market who present the facts in a somewhat different manner. Some data visualisation tools specialise in a single style of chart or map and excel at it. Those tools are also among the "best" tools available. Finally, there are financial concerns. While a larger price tag does not inherently disqualify a tool, it must be justified in terms of greater support, features, and overall value.

1. Tableau

One of the most widely used data visualization tools, Tableau, offers interactive visualization solutions to more than 57,000 companies.

Providing integration for advanced databases, including Teradata, SAP, My SQL, Amazon AWS, and Hadoop, Tableau efficiently creates visualizations and graphics from large, constantly-evolving datasets used for artificial intelligence, machine learning, and Big Data applications.

The Pros of Tableau:

Excellent visualization capabilities

Easy to use

Top class performance

Supports connectivity with diverse data sources

Mobile Responsive

Has an informative community

The Cons of Tableau:

The pricing is a bit on the higher side

Auto-refresh and report scheduling options are not available

2. Dundas BI

Dundas BI offers highly-customizable data visualizations with interactive scorecards, maps, gauges, and charts, optimizing the creation of ad-hoc, multi-page reports. By providing users full control over visual elements, Dundas BI simplifies the complex operation of cleansing, inspecting, transforming, and modeling big datasets.

The Pros of Dundas BI:

The Cons of Dundas BI:

No option for predictive analytics

3D charts not supported

3. JupyteR

A web-based application, JupyteR, is one of the top-rated data visualization tools that enable users to create and share documents containing visualizations, equations, narrative text, and live code. JupyteR is ideal for data cleansing and transformation, statistical modeling, numerical simulation, interactive computing, and machine learning.

The Pros of JupyteR:

The Cons of JupyteR:

Tough to collaborate

At times code reviewing becomes complicated

4. Zoho Reports

Zoho Reports, also known as Zoho Analytics, is a comprehensive data visualization tool that integrates Business Intelligence and online reporting services, which allow quick creation and sharing of extensive reports in minutes. The high-grade visualization tool also supports the import of Big Data from major databases and applications.

The Pros of Zoho Reports:

The Cons of Zoho Reports:

User training needs to be improved

The dashboard becomes confusing when there are large volumes of data

5. Google Charts

One of the major players in the data visualization market space, Google Charts, coded with SVG and HTML5, is famed for its capability to produce graphical and pictorial data visualizations. Google Charts offers zoom functionality, and it provides users with unmatched cross-platform compatibility with iOS, Android, and even the earlier versions of the Internet Explorer browser.

The Pros of Google Charts:

The Cons of Google Charts:

6. Visual.ly

Visual.ly is one of the data visualization tools on the market, renowned for its impressive distribution network that illustrates project outcomes. Employing a dedicated creative team for data visualization services, Visual.ly streamlines the process of data import and outsource, even to third parties.

The Pros of Visual.ly:

The Cons of Visual.ly:

7. RAW

RAW, better-known as RawGraphs, works with delimited data such as TSV file or CSV file. It serves as a link between data visualization and spreadsheets. Featuring a range of non-conventional and conventional layouts, RawGraphs provides robust data security even though it is a web-based application.

The Pros of RAW:

Simple interface

Super-fast visual feedback

Offers a high-level platform for arranging, keeping, and reading user data

Easy-to-use mapping feature

Superb readability for visual graphics

Excellent scalability option

The Cons of RAW:

Non-availability of log scales

Not user intuitive

8. IBM Watson

Named after IBM founder Thomas J. Watson, this high-caliber data visualization tool uses analytical components and artificial intelligence to detect insights and patterns from both unstructured and structured data. Leveraging NLP ( Natural Language Processing), IBM Watson's intelligent, self-service visualization tool guides users through the entire insight discovery operation.

The Pros of IBM Watson:

The Cons of IBM Watson:

Customer support needs improvement

High-cost maintenance

9. Sisense

Regarded as one of the most agile data visualization tools, Sisense gives users access to instant data analytics anywhere, at any time. The best-in-class visualization tool can identify key data patterns and summarize statistics to help decision-makers make data-driven decisions.

The Pros of Sisense:

Ideal for mission-critical projects involving massive datasets

Reliable interface

High-class customer support

Quick upgrades

Flexibility of seamless customization

The Cons of Sisense:

10. Plotly

An open-source data visualization tool, Plotly offers full integration with analytics-centric programming languages like Matlab, Python, and R, which enables complex visualizations. Widely used for collaborative work, disseminating, modifying, creating, and sharing interactive, graphical data, Plotly supports both on-premise installation and cloud deployment.

The Pros of Plotly:

The Cons of Plotly:

11. Data Wrapper

Data Wrapper is one of the very few data visualization tools on the market that is available for free. It is popular among media enterprises because of its inherent ability to quickly create charts and present graphical statistics on Big Data. Featuring a simple and intuitive interface, Data Wrapper allows users to create maps and charts that they can easily embed into reports.

The Pros of Data Wrapper:

The Cons of Data Wrapper:

Building complex charts like Sankey is a problem

Security is an issue as it is an open-source tool

12. Highcharts

Deployed by seventy-two of the world's top hundred companies, the Highcharts tool is perfect for visualization of streaming big data analytics. Running on Javascript API and offering integration with jQuery, Highcharts provides support for cross-browser functionalities that facilitates easy access to interactive visualizations.

The Pros of Highcharts:

The Cons of Highcharts:

Not ideal for small organizations

13. Fusioncharts

Fusioncharts is one of the most popular and widely-adopted data visualization tools. The Javascript-based, top-of-the-line visualization tool offers ninety different chart building packages that integrate with major frameworks and platforms, offering users significant flexibility.

The Pros of Fusioncharts:

The Cons of Fusioncharts:

14. Power BI

Power BI, Microsoft's easy-to-use data visualization tool, is available for both on-premise installation and deployment on the cloud infrastructure. Power BI is one of the most complete data visualization tools that supports a myriad of backend databases, including Teradata, Salesforce, PostgreSQL, Oracle, Google Analytics, Github, Adobe Analytics, Azure, SQL Server, and Excel. The enterprise-level tool creates stunning visualizations and delivers real-time insights for fast decision-making.

The Pros of Power BI:

No requirement for specialized tech support

Easily integrates with existing applications

Personalized, rich dashboard

High-grade security

No speed or memory constraints

Compatible with Microsoft products

The Cons of Power BI:

Cannot work with varied, multiple datasets

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15. QlikView

A major player in the data visualization market, Qlikview provides solutions to over 40,000 clients in 100 countries. Qlikview's data visualization tool, besides enabling accelerated, customized visualizations, also incorporates a range of solid features, including analytics, enterprise reporting, and Business Intelligence capabilities.

The Pros of QlikView:

The Cons of QlikView:

16. Infogram

Infogram is one of the most popular software programmes on the internet today. It is a web-based tool for creating infographics and visualising data. It is primarily intended to assist all users in quickly and simply creating interesting and interactive reports, infographics, and dashboards with data-driven information and captivating images. This particular solution provides customers with over 550 maps and 35 charts, 20 ready-made design templates, numerous pictures and icons, a drag-and-drop editor, and other features. Even someone who is new to the sector may quickly learn how to utilise this programme.

It has a simple editor that allows users to modify the colours and styles of their visualisations, add corporate logos, and adjust the display choices. In addition, the users will be granted the right to use over a million icons, GIFs, and photos in their visualisations. Users may add connections to generate traffic to their website using interactive charts, which allow audiences to examine data using Infogram tabs. Reports that are interactive and shareable may also be developed and incorporated, with metrics to measure audience interaction.

17. ChartBlocks

ChartBlocks selects the appropriate data segment to create a chart and manages the whole import process. It may import information from virtually any source. It enhances many sharing options that set the chart on the website and instantly share it. It contains hundreds of customization and design choices that influence various aspects of the chart. The Wizard feature selects and selects the appropriate data for the chart using the basic chart design wizard. ChartBlocks' data import capabilities enable data to be swiftly imported from any source. It aids in the import of proper data from the target source and the creation of the chart. And all of this happens in a matter of minutes. To create a chart, no code is necessary.

It allows for the creation of a chart in minutes, as well as the use of a chart designer and the selection of hundreds of chart kinds, which may be adjusted as needed. It can also gather data from nearly any source and use it to make visualisations. The data import wizard walks you through each step of the procedure. It easily embeds charts into any website of your choice and distributes them.

The same sharing functionality is available in the built-in social media sharing tools. It is known to interface directly with Facebook and Twitter. It also has a function that allows the charts to be exported as editable vectors and graphics.

18. D3.js

With Data-Driven Document, you can use any browser to bind data to a DOM in a document, allowing you to manipulate documents from anywhere. Transforming data involves selecting selections of nodes and manipulating them individually. You can easily change and alter node attributes, register event listeners, change nodes, alter HTML or text content, and access the document's underlying DOM by working with functions of data (styles, attributes, and other properties). You can associate operations (updates, additions, and deletions) with nodes to improve performance. You can build new functions using the function factory, as well as using the graphical primitives included. Geographic coordinates can be retrieved using a function as opposed to a constant. Properties can be reused by having data bound to the documents.

It uses HTML, SVG and CSS to create graphics from data, for example generating a table in HTML from data. Using animated transitions and high performance, you can easily visualize data in bar charts and graphics, support large amounts of data, and enjoy dynamic interaction and animation in a 3D environment with large amounts of data.

19. Chart.js

Chart.js is a popular JavaScript charting toolkit that is open source. It is a Data Visualization Software that will help you visualise data. Because it is open-source, it is maintained by the entire community. It has support for eight various types of charts, including pies, lines, and bars. The good news is that all of these are really responsive. All you have to do is put up your chart, and the library will make sure it is readable. It has 53.7k GitHub stars and a strong ecosystem providing wrappers for Vue, React, Ember, and more frameworks. The library draws the charts on the browser canvas. It is an independent project with numerous community contributors. Chart.js provides eight different types of chart bar charts, but also bubble charts, scatter charts, line charts, and polar charts.

20. Grafana

Grafana open source is a free and open source visualisation and analytics tool. It enables you to query, display, alert on, and examine metrics, logs, and traces stored everywhere. It includes tools for transforming time-series database (TSDB) data into informative graphs and visualisations.

It also has a Graohana cloud component. It is an OpenSaaS logging and metrics platform that is highly available, quick, and fully controlled. The program provides all of the features you love about Grafana, but Grafana Labs hosts and manages the program for you.

Grafana Enterprise is Grafana's commercial edition, which offers capabilities not present in the open source version. Grafana Corporate includes enterprise data sources, sophisticated authentication choices, expanded permission restrictions, 24x7x365 support, and core team training.

21. Chartist.js

Chartist.js is an online application that allows you to build highly customizable responsive charts that highlight important data and construct a library or libraries. Chartist.js encapsulates the given data in a library for usage in a user-friendly framework. Chartist.js is now used to create libraries in a variety of projects, including Chartist JSF (Java Server Faces Component), node chartist (node package for server-side charts, ng-chartist.js (Angular Directive), Table press Chartist (WordPress/ tablepress extension), Ember - cli - chartist (Ember Addon), react chartist (react component), etc.

Chartist.js is user-friendly since it is compatible with a variety of browsers, making it simple to work with any of them. The browsers enable the use of several remarkable capabilities, such as general browser support, sophisticated CSS animations, SVG animations, multi-line labels, with SMIL, and responsive option override.

These are critical properties that every browser wants to have in order to provide reliable information. These capabilities allow Chartist.js to create charts that have an animation component, making them presentable and simple to read.

22. Sigma.js

Sigma is a JavaScript library for drawing graphs. It enables developers to incorporate network exploration into rich online applications and makes it simple to publish networks on websites.

The Sigma.js layout is fantastic.

It enables individuals to follow up with interest as soon as possible.

Sigma.js's performance is currently satisfactory.

Sigma.js support is fantastic and quite helpful.

Good software must be tried.

23. Polymaps

Polymaps, a collaboration between Stamen and SimpleGeo, is a free JavaScript library for image and vector tiled maps that use SVG. The library enables the creation of interactive and dynamic maps in web browsers, as well as the rapid display of datasets and support for a broad range of visual presentations for vector data (tiled). Cartography from CloudMade, OpenStreetMap, Bing, and other image-based web map suppliers is supported by Polymaps. It can load data at all scales and works well for displaying information from the country level down to the local level. It displays information using Scalable Vector Graphics (SVG), which allows users to simply create data design using CSS rules. This also saves users from having to learn new scripts because they may do the majority of jobs using scripts to which they are already accustomed.
Check out the video below that explains what Data Visualization is, Why we use Data Visualization, major considerations for Data Visualization and the basics of different types of graphs.

Learning Data Visualization Tools to Further Your Career

Business analysts are the most sought-after IT professionals. Today, there is an increased demand for business analysts, but most organizations are facing a severe skills gap, failing to fill key roles. The rising shortage of new talent is a great opportunity for business analyst aspirants. Learning data visualization tools from an industry-recognized educational institution, such as Simplilearn, can enable them to grab better opportunities and get hired in their dream roles.

Simplilearn's Business Analysis Certification Program, in partnership with Purdue University and endorsed by the International Institute of Business Analysis (IIBA), teaches you 30+ in-demand skills and tools with hands-on industry training in 14+ real-life projects. Join the world's #1 online bootcamp here.

What Is Data Visualization?

Data visualization is the process of graphical representation of data in the form of geographic maps, charts, sparklines, infographics, heat maps, or statistical graphs.

Data presented through visual elements is easy to understand and analyze, enabling the effective extraction of actionable insights from the data. Relevant stakeholders can then use the findings to make more efficient real-time decisions.

Data visualization tools, incorporating support for streaming data, AI integration, embeddability, collaboration, interactive exploration, and self-service capabilities, facilitate the visual representation of data.

Here are 15 top-notch data visualization tools that are gaining market recognition for their impressive performance and usability.

What Are Data Visualization Tools?

Data is becoming increasingly important every day. For any organisation, you can understand how important data is while making crucial decisions. For the same reason, data visualisation is grabbing people's attention. Modern data visualisation tools and advanced software are on the market. A data visualisation tool is a software that is used to visualise data. The features of each tool vary, but at their most basic, they allow you to input a dataset and graphically alter it. Most, but not all, come with pre-built templates for creating simple visualisations.

What Do the Best Data Visualization Tools Have in Common?

All of the technologies available on the market for data visualisation have something or another feature in common. The first advantage is their simplicity of usage. There are two types of software that you will most likely encounter: those that are easy to use and those that are really difficult to visualise data. Some include good documentation and tutorials and are constructed in user-friendly ways. Others, regardless of their other qualities, are missing in certain areas, excluding them from any list of "best" tools. The one thing you should ensure is that the software can handle large amounts of data and many kinds of data in a single display.

The better software can also generate a variety of charts, graphs, and maps kinds. Obviously, there will be others in the market who present the facts in a somewhat different manner. Some data visualisation tools specialise in a single style of chart or map and excel at it. Those tools are also among the "best" tools available. Finally, there are financial concerns. While a larger price tag does not inherently disqualify a tool, it must be justified in terms of greater support, features, and overall value.

1. Tableau

One of the most widely used data visualization tools, Tableau, offers interactive visualization solutions to more than 57,000 companies.

Providing integration for advanced databases, including Teradata, SAP, My SQL, Amazon AWS, and Hadoop, Tableau efficiently creates visualizations and graphics from large, constantly-evolving datasets used for artificial intelligence, machine learning, and Big Data applications.

The Pros of Tableau:

Excellent visualization capabilities

Easy to use

Top class performance

Supports connectivity with diverse data sources

Mobile Responsive

Has an informative community

The Cons of Tableau:

The pricing is a bit on the higher side

Auto-refresh and report scheduling options are not available

2. Dundas BI

Dundas BI offers highly-customizable data visualizations with interactive scorecards, maps, gauges, and charts, optimizing the creation of ad-hoc, multi-page reports. By providing users full control over visual elements, Dundas BI simplifies the complex operation of cleansing, inspecting, transforming, and modeling big datasets.

The Pros of Dundas BI:

The Cons of Dundas BI:

No option for predictive analytics

3D charts not supported

3. JupyteR

A web-based application, JupyteR, is one of the top-rated data visualization tools that enable users to create and share documents containing visualizations, equations, narrative text, and live code. JupyteR is ideal for data cleansing and transformation, statistical modeling, numerical simulation, interactive computing, and machine learning.

The Pros of JupyteR:

The Cons of JupyteR:

Tough to collaborate

At times code reviewing becomes complicated

4. Zoho Reports

Zoho Reports, also known as Zoho Analytics, is a comprehensive data visualization tool that integrates Business Intelligence and online reporting services, which allow quick creation and sharing of extensive reports in minutes. The high-grade visualization tool also supports the import of Big Data from major databases and applications.

The Pros of Zoho Reports:

The Cons of Zoho Reports:

User training needs to be improved

The dashboard becomes confusing when there are large volumes of data

5. Google Charts

One of the major players in the data visualization market space, Google Charts, coded with SVG and HTML5, is famed for its capability to produce graphical and pictorial data visualizations. Google Charts offers zoom functionality, and it provides users with unmatched cross-platform compatibility with iOS, Android, and even the earlier versions of the Internet Explorer browser.

The Pros of Google Charts:

The Cons of Google Charts:

6. Visual.ly

Visual.ly is one of the data visualization tools on the market, renowned for its impressive distribution network that illustrates project outcomes. Employing a dedicated creative team for data visualization services, Visual.ly streamlines the process of data import and outsource, even to third parties.

The Pros of Visual.ly:

The Cons of Visual.ly:

7. RAW

RAW, better-known as RawGraphs, works with delimited data such as TSV file or CSV file. It serves as a link between data visualization and spreadsheets. Featuring a range of non-conventional and conventional layouts, RawGraphs provides robust data security even though it is a web-based application.

The Pros of RAW:

Simple interface

Super-fast visual feedback

Offers a high-level platform for arranging, keeping, and reading user data

Easy-to-use mapping feature

Superb readability for visual graphics

Excellent scalability option

The Cons of RAW:

Non-availability of log scales

Not user intuitive

8. IBM Watson

Named after IBM founder Thomas J. Watson, this high-caliber data visualization tool uses analytical components and artificial intelligence to detect insights and patterns from both unstructured and structured data. Leveraging NLP ( Natural Language Processing), IBM Watson's intelligent, self-service visualization tool guides users through the entire insight discovery operation.

The Pros of IBM Watson:

The Cons of IBM Watson:

Customer support needs improvement

High-cost maintenance

9. Sisense

Regarded as one of the most agile data visualization tools, Sisense gives users access to instant data analytics anywhere, at any time. The best-in-class visualization tool can identify key data patterns and summarize statistics to help decision-makers make data-driven decisions.

The Pros of Sisense:

Ideal for mission-critical projects involving massive datasets

Reliable interface

High-class customer support

Quick upgrades

Flexibility of seamless customization

The Cons of Sisense:

10. Plotly

An open-source data visualization tool, Plotly offers full integration with analytics-centric programming languages like Matlab, Python, and R, which enables complex visualizations. Widely used for collaborative work, disseminating, modifying, creating, and sharing interactive, graphical data, Plotly supports both on-premise installation and cloud deployment.

The Pros of Plotly:

The Cons of Plotly:

11. Data Wrapper

Data Wrapper is one of the very few data visualization tools on the market that is available for free. It is popular among media enterprises because of its inherent ability to quickly create charts and present graphical statistics on Big Data. Featuring a simple and intuitive interface, Data Wrapper allows users to create maps and charts that they can easily embed into reports.

The Pros of Data Wrapper:

The Cons of Data Wrapper:

Building complex charts like Sankey is a problem

Security is an issue as it is an open-source tool

12. Highcharts

Deployed by seventy-two of the world's top hundred companies, the Highcharts tool is perfect for visualization of streaming big data analytics. Running on Javascript API and offering integration with jQuery, Highcharts provides support for cross-browser functionalities that facilitates easy access to interactive visualizations.

The Pros of Highcharts:

The Cons of Highcharts:

Not ideal for small organizations

13. Fusioncharts

Fusioncharts is one of the most popular and widely-adopted data visualization tools. The Javascript-based, top-of-the-line visualization tool offers ninety different chart building packages that integrate with major frameworks and platforms, offering users significant flexibility.

The Pros of Fusioncharts:

The Cons of Fusioncharts:

14. Power BI

Power BI, Microsoft's easy-to-use data visualization tool, is available for both on-premise installation and deployment on the cloud infrastructure. Power BI is one of the most complete data visualization tools that supports a myriad of backend databases, including Teradata, Salesforce, PostgreSQL, Oracle, Google Analytics, Github, Adobe Analytics, Azure, SQL Server, and Excel. The enterprise-level tool creates stunning visualizations and delivers real-time insights for fast decision-making.

The Pros of Power BI:

No requirement for specialized tech support

Easily integrates with existing applications

Personalized, rich dashboard

High-grade security

No speed or memory constraints

Compatible with Microsoft products

The Cons of Power BI:

Cannot work with varied, multiple datasets

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15. QlikView

A major player in the data visualization market, Qlikview provides solutions to over 40,000 clients in 100 countries. Qlikview's data visualization tool, besides enabling accelerated, customized visualizations, also incorporates a range of solid features, including analytics, enterprise reporting, and Business Intelligence capabilities.

The Pros of QlikView:

The Cons of QlikView:

16. Infogram

Infogram is one of the most popular software programmes on the internet today. It is a web-based tool for creating infographics and visualising data. It is primarily intended to assist all users in quickly and simply creating interesting and interactive reports, infographics, and dashboards with data-driven information and captivating images. This particular solution provides customers with over 550 maps and 35 charts, 20 ready-made design templates, numerous pictures and icons, a drag-and-drop editor, and other features. Even someone who is new to the sector may quickly learn how to utilise this programme.

It has a simple editor that allows users to modify the colours and styles of their visualisations, add corporate logos, and adjust the display choices. In addition, the users will be granted the right to use over a million icons, GIFs, and photos in their visualisations. Users may add connections to generate traffic to their website using interactive charts, which allow audiences to examine data using Infogram tabs. Reports that are interactive and shareable may also be developed and incorporated, with metrics to measure audience interaction.

17. ChartBlocks

ChartBlocks selects the appropriate data segment to create a chart and manages the whole import process. It may import information from virtually any source. It enhances many sharing options that set the chart on the website and instantly share it. It contains hundreds of customization and design choices that influence various aspects of the chart. The Wizard feature selects and selects the appropriate data for the chart using the basic chart design wizard. ChartBlocks' data import capabilities enable data to be swiftly imported from any source. It aids in the import of proper data from the target source and the creation of the chart. And all of this happens in a matter of minutes. To create a chart, no code is necessary.

It allows for the creation of a chart in minutes, as well as the use of a chart designer and the selection of hundreds of chart kinds, which may be adjusted as needed. It can also gather data from nearly any source and use it to make visualisations. The data import wizard walks you through each step of the procedure. It easily embeds charts into any website of your choice and distributes them.

The same sharing functionality is available in the built-in social media sharing tools. It is known to interface directly with Facebook and Twitter. It also has a function that allows the charts to be exported as editable vectors and graphics.

18. D3.js

With Data-Driven Document, you can use any browser to bind data to a DOM in a document, allowing you to manipulate documents from anywhere. Transforming data involves selecting selections of nodes and manipulating them individually. You can easily change and alter node attributes, register event listeners, change nodes, alter HTML or text content, and access the document's underlying DOM by working with functions of data (styles, attributes, and other properties). You can associate operations (updates, additions, and deletions) with nodes to improve performance. You can build new functions using the function factory, as well as using the graphical primitives included. Geographic coordinates can be retrieved using a function as opposed to a constant. Properties can be reused by having data bound to the documents.

It uses HTML, SVG and CSS to create graphics from data, for example generating a table in HTML from data. Using animated transitions and high performance, you can easily visualize data in bar charts and graphics, support large amounts of data, and enjoy dynamic interaction and animation in a 3D environment with large amounts of data.

19. Chart.js

Chart.js is a popular JavaScript charting toolkit that is open source. It is a Data Visualization Software that will help you visualise data. Because it is open-source, it is maintained by the entire community. It has support for eight various types of charts, including pies, lines, and bars. The good news is that all of these are really responsive. All you have to do is put up your chart, and the library will make sure it is readable. It has 53.7k GitHub stars and a strong ecosystem providing wrappers for Vue, React, Ember, and more frameworks. The library draws the charts on the browser canvas. It is an independent project with numerous community contributors. Chart.js provides eight different types of chart bar charts, but also bubble charts, scatter charts, line charts, and polar charts.

20. Grafana

Grafana open source is a free and open source visualisation and analytics tool. It enables you to query, display, alert on, and examine metrics, logs, and traces stored everywhere. It includes tools for transforming time-series database (TSDB) data into informative graphs and visualisations.

It also has a Graohana cloud component. It is an OpenSaaS logging and metrics platform that is highly available, quick, and fully controlled. The program provides all of the features you love about Grafana, but Grafana Labs hosts and manages the program for you.

Grafana Enterprise is Grafana's commercial edition, which offers capabilities not present in the open source version. Grafana Corporate includes enterprise data sources, sophisticated authentication choices, expanded permission restrictions, 24x7x365 support, and core team training.

21. Chartist.js

Chartist.js is an online application that allows you to build highly customizable responsive charts that highlight important data and construct a library or libraries. Chartist.js encapsulates the given data in a library for usage in a user-friendly framework. Chartist.js is now used to create libraries in a variety of projects, including Chartist JSF (Java Server Faces Component), node chartist (node package for server-side charts, ng-chartist.js (Angular Directive), Table press Chartist (WordPress/ tablepress extension), Ember - cli - chartist (Ember Addon), react chartist (react component), etc.

Chartist.js is user-friendly since it is compatible with a variety of browsers, making it simple to work with any of them. The browsers enable the use of several remarkable capabilities, such as general browser support, sophisticated CSS animations, SVG animations, multi-line labels, with SMIL, and responsive option override.

These are critical properties that every browser wants to have in order to provide reliable information. These capabilities allow Chartist.js to create charts that have an animation component, making them presentable and simple to read.

22. Sigma.js

Sigma is a JavaScript library for drawing graphs. It enables developers to incorporate network exploration into rich online applications and makes it simple to publish networks on websites.

The Sigma.js layout is fantastic.

It enables individuals to follow up with interest as soon as possible.

Sigma.js's performance is currently satisfactory.

Sigma.js support is fantastic and quite helpful.

Good software must be tried.

23. Polymaps

Polymaps, a collaboration between Stamen and SimpleGeo, is a free JavaScript library for image and vector tiled maps that use SVG. The library enables the creation of interactive and dynamic maps in web browsers, as well as the rapid display of datasets and support for a broad range of visual presentations for vector data (tiled). Cartography from CloudMade, OpenStreetMap, Bing, and other image-based web map suppliers is supported by Polymaps. It can load data at all scales and works well for displaying information from the country level down to the local level. It displays information using Scalable Vector Graphics (SVG), which allows users to simply create data design using CSS rules. This also saves users from having to learn new scripts because they may do the majority of jobs using scripts to which they are already accustomed.
Check out the video below that explains what Data Visualization is, Why we use Data Visualization, major considerations for Data Visualization and the basics of different types of graphs.

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Simplilearn's Business Analysis Certification Program, in partnership with Purdue University and endorsed by the International Institute of Business Analysis (IIBA), teaches you 30+ in-demand skills and tools with hands-on industry training in 14+ real-life projects. Join the world's #1 online bootcamp here.

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23 Best Data Visualization Tools for 2022

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According to a Fortune Business Insights report, the data visualization market in 2019 was estimated at $8.85 billion. By 2027, the market worth is expected to be $19.20 billion at a compound annual growth rate of 10.2%.

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המשיכו להרחיב את הידע שלכם על ויזואליזציה בעזרת מאמרים און ליין.

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What is data visualization?

Data visualization is the graphical representation of information and data. By using v isual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends, outliers, and patterns in data. Additionally, it provides an excellent way for employees or business owners to present data to non-technical audiences without confusion.

In the world of Big Data, data visualization tools and technologies are essential to analyze massive amounts of information and make data-driven decisions.

What are the advantages and disadvantages of data visualization?

​​​​Something as simple as presenting data in graphic format may seem to have no downsides. But sometimes data can be misrepresented or misinterpreted when placed in the wrong style of data visualization. When choosing to create a data visualization, it's best to keep both the advantages and disadvantages in mind.

Advantages

Our eyes are drawn to colors and patterns. We can quickly identify red from blue, and squares from circles. Our culture is visual, including everything from art and advertisements to TV and movies. Data visualization is another form of visual art that grabs our interest and keeps our eyes on the message. When we see a chart, we quickly see trends and outliers. If we can see something, we internalize it quickly. It's storytelling with a purpose. If you've ever stared at a massive spreadsheet of data and couldn't see a trend, you know how much more effective a visualization can be.

Some other advantages of data visualization include:

Easily sharing information.

Interactively explore opportunities.

Visualize patterns and relationships.

Disadvantages

While there are many advantages, some of the disadvantages may seem less obvious. For example, when viewing a visualization with many different datapoints, it's easy to make an inaccurate assumption. Or sometimes the visualization is just designed wrong so that it's biased or confusing.

Some other disadvantages include:

Biased or inaccurate information.

Correlation doesn't always mean causation.

Core messages can get lost in translation.

Why data visualization is important

The importance of data visualization is simple: it helps people see, interact with, and better understand data. Whether simple or complex, the right visualization can bring everyone on the same page, regardless of their level of expertise.

It's hard to think of a professional industry that doesn't benefit from making data more understandable. Every STEM field benefits from understanding data-and so do fields in government, finance, marketing, history, consumer goods, service industries, education, sports, and so on.

While we'll always wax poetically about data visualization (you're on the Tableau website, after all) there are practical, real-life applications that are undeniable. And, since visualization is so prolific, it's also one of the most useful professional skills to develop. The better you can convey your points visually, whether in a dashboard or a slide deck, the better you can leverage that information. The concept of the citizen data scientist is on the rise. Skill sets are changing to accommodate a data-driven world. It is increasingly valuable for professionals to be able to use data to make decisions and use visuals to tell stories of when data informs the who, what, when, where, and how.

While traditional education typically draws a distinct line between creative storytelling and technical analysis, the modern professional world also values those who can cross between the two: data visualization sits right in the middle of analysis and visual storytelling.

Data visualization and big data

As the "age of Big Data" kicks into high gear, visualization is an increasingly key tool to make sense of the trillions of rows of data generated every day. Data visualization helps to tell stories by curating data into a form easier to understand, highlighting the trends and outliers. A good visualization tells a story, removing the noise from data and highlighting useful information.

However, it's not simply as easy as just dressing up a graph to make it look better or slapping on the "info" part of an infographic. Effective data visualization is a delicate balancing act between form and function. The plainest graph could be too boring to catch any notice or it make tell a powerful point; the most stunning visualization could utterly fail at conveying the right message or it could speak volumes. The data and the visuals need to work together, and there's an art to combining great analysis with great storytelling.

Learn more about big data.

Examples of data visualization

Of course, one of the best ways to understand data visualization is to see it. What a crazy concept! With public data visualization galleries and data everywhere online, it can be overwhelming to know where to start. Tableau's own public gallery shows off loads of visualizations made with the free Tableau Public tool, we feature some common starter business dashboards as usable templates, and Viz of the Day collects some of the best community creations. We've also collected 1 0 of the best examples of data visualization of all time, with examples that map historical conquests, analyze film scripts, reveal hidden causes of mortality, and more.

Different types of visualizations

When you think of data visualization, your first thought probably immediately goes to simple bar graphs or pie charts. While these may be an integral part of visualizing data and a common baseline for many data graphics, the right visualization must be paired with the right set of information. Simple graphs are only the tip of the iceberg. There's a whole selection of visualization methods to present data in effective and interesting ways.

General Types of Visualizations:

Chart: Information presented in a tabular, graphical form with data displayed along two axes. Can be in the form of a graph, diagram, or map. Learn more.

Table: A set of figures displayed in rows and columns. Learn more.

Graph: A diagram of points, lines, segments, curves, or areas that represents certain variables in comparison to each other, usually along two axes at a right angle.

Geospatial: A visualization that shows data in map form using different shapes and colors to show the relationship between pieces of data and specific locations. Learn more.

Infographic: A combination of visuals and words that represent data. Usually uses charts or diagrams.

Dashboards: A collection of visualizations and data displayed in one place to help with analyzing and presenting data. Learn more.

More specific examples

Area Map: A form of geospatial visualization, area maps are used to show specific values set over a map of a country, state, county, or any other geographic location. Two common types of area maps are choropleths and isopleths. Learn more.

Bar Chart: Bar charts represent numerical values compared to each other. The length of the bar represents the value of each variable. Learn more.

Box-and-whisker Plots: These show a selection of ranges (the box) across a set measure (the bar). Learn more.

Bullet Graph: A bar marked against a background to show progress or performance against a goal, denoted by a line on the graph. Learn more.

Gantt Chart: Typically used in project management, Gantt charts are a bar chart depiction of timelines and tasks. Learn more.

Heat Map: A type of geospatial visualization in map form which displays specific data values as different colors (this doesn't need to be temperatures, but that is a common use). Learn more.

Highlight Table: A form of table that uses color to categorize similar data, allowing the viewer to read it more easily and intuitively. Learn more.

Histogram: A type of bar chart that split a continuous measure into different bins to help analyze the distribution. Learn more.

Pie Chart: A circular chart with triangular segments that shows data as a percentage of a whole. Learn more.

Treemap: A type of chart that shows different, related values in the form of rectangles nested together. Learn more.

Visualization tools and software

There are dozens of tools for data visualization and data analysis. These range from simple to complex, from intuitive to obtuse. Not every tool is right for every person looking to learn visualization techniques, and not every tool can scale to industry or enterprise purposes. If you'd like to learn more about the options, feel free to read up here or dive into detailed third-party analysis like the Gartner Magic Quadrant.

Also, remember that good data visualization theory and skills will transcend specific tools and products. When you're learning this skill, focus on best practices and explore your own personal style when it comes to visualizations and dashboards. Data visualization isn't going away any time soon, so it's important to build a foundation of analysis and storytelling and exploration that you can carry with you regardless of the tools or software you end up using.

Learn more about data visualizations

If you're feeling inspired or want to learn more, there are tons of resources to tap into. Data visualization and data journalism are full of enthusiastic practitioners eager to share their tips, tricks, theory, and more.

Blogs about data visualization

See our list of great data visualization blogs full of examples, inspiration, and educational resources. The experts who write books and teach classes about the theory behind data visualization also tend to keep blogs where they analyze the latest trends in the field and discuss new vizzes. Many will offer critiques on modern graphics or write tutorials to create effective visualizations. Others will collect many different data visualizations from around the web in order to highlight the most intriguing ones. Blogs are a great way to learn more about specific subsets of data visualization or to look for relatable inspiration from well-done projects.
See our list of the best data visualization blogs.
Books about data visualization

Read our list of great books about data visualization theory and practice. While blogs can keep up with the changing field of data visualization, books focus on where the theory stays constant. Humans have been trying to present data in a visual form throughout our entire existence. One of the earlier books about data visualization, originally published in 1983, set the stage for data visualization to come and still remains relevant to this day. More current books still deal with theory and techniques, offering up timeless examples and practical tips. Some even take completed projects and present the visual graphics in book form as an archival display.
See our list of the best data visualization books.
Courses and Training

There are plenty of great paid and free courses and resources on data visualization out there, including right here on the Tableau website. There are videos, articles, and whitepapers for everyone from beginners to data rockstars. When it comes to third-party courses, however, we won't provide specific suggestions in this article at this time.
Explore Tableau's trainings.

Learn more about Academic Programs

When it comes to elevating people with the power of data, Tableau makes it easy to incorporate visual analytics into any curriculum. Our Academic Programs provides students and instructors around the world with free access to Tableau Desktop, Prep, and eLearning.

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What Is Data Visualization? Definition, Examples, And Learning Resources

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Data visualization is the graphical representation of information. It uses visual elements like charts to provide an accessible way to see and understand data.

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במהלך השיעור, המורה יעקוב אחרי הדיונים בקבוצות ויבחן את התרגולים השונים של התלמידים.

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בסוף השיעור, כל תלמיד ייצור ויזואליזציה מלאה ויקבל משוב מהמורה.

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דיון מסכם על היתרונות והאתגרים של ויזואליזציית נתונים.

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כל תלמיד יכתוב על כרטיס יציאה אלמנט אחד חדש שלמד היום ואלמנט אחד שעדיין לא ברור לו.

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צרו ויזואליזציה נוספת בעזרת כלי מקוון כלשהו והגישו את העבודה בשבוע הבא.

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A free and open source tool for data visualization.