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Introduction to the Basics of Data Visualization

Data analytics is a process of analyzing raw data to find trends and patterns. However, it involves many different techniques for achieving many other objectives.


Some components of the data analytics process can prove helpful for many initiatives. Analyzing data components will help you determine your company’s performance, history, and future.


An approach to Data Science is geared toward finding meaningful correlations between large datasets, while a strategy to Data Analytics is geared toward extracting specific insights from data. It addresses particular questions by Data Science through the use of Data Analytics.


Data visualization represents the data and information in a visual and easily consumable format. It makes data trends, outliers, and patterns more accessible with the help of visual elements such as charts, graphs, and maps.


Table of Content


What is Data Visualization?

Importance of Data Visualization lies in process of representing data in a graphical or visual format, making it easier to understand and analyze. Importance of Data Visualization is vital for graphical representation of data and information, enabling businesses to draw valuable insights and make informed decisions. It is a key aspect of data visualization in data science, allowing individuals to better understand and analyze data. In this article, we will explore the importance of data visualization in data science, the different types of visualization techniques, Data Visualization examples and how it is used. Read more on Business Analytics and Data Science Course.


Importance of data visualization

  1. Facilitates Understanding – Data visualization makes it easier for people to understand complex data sets and identify key patterns, trends, and insights. Thus, establishing importance of Data Visualization.
  2. Increases Efficiency – With data visualization, businesses can quickly and efficiently analyze large amounts of data, making it possible to identify patterns and trends that might otherwise be missed has elevated importance of Data Visualization.
  3. Improves Communication – Visual representations of data can be more impactful and persuasive than raw data, making it easier to communicate key insights and recommendations to stakeholders. Also check What is Big Data Architecture?

Types of Data Visualization

  1. Charts and Graphs – Bar charts, line charts, scatter plots, and pie charts are common types of data visualizations that are used to represent data.
  2. Maps – Maps are used to represent geographic data, enabling businesses to identify patterns and trends in specific regions.
  3. Dashboards – Dashboards are a collection of data visualizations that provide a holistic view of a business or organization’s performance. Read more on An Introduction to Data Structure: Features, Types and Applications. And Understanding the Types Data Structures and Their Applications.

How is Data Visualization Used and Importance of Data Visualization:

  1. Business Intelligence – Data visualization is used in business intelligence to help businesses make informed decisions based on data. This is one of the excellent Data Visualization examples.
  2. Reporting – Another Data Visualization examples is its use in reporting to provide stakeholders with a clear and concise view of key data.
  3. Research – Data Visualization examples are used in research to help researchers identify patterns and trends in large data sets.

How does Data Visualization help?

Data visualization can play a vital role in any field, whether in business or a career, as it can help in the representation of data more effectively. In business intelligence, the importance of data visualization has witnessed an uptrend because it is responsible for analyzing the raw data, modeling it, and delivering the data to the analyst to interpret the findings and take the appropriate decisions.


Data scientists in advanced analytics are developing machine learning algorithms to create visualizations that are easier to understand and interpret. The use of data visualization enables universal, fast, and effective communication of information.


Using this strategy, a business can identify what areas need improvement, how customer satisfaction is impacted, and where specific products should be sold (to whom and where). With the help of visualized data, a better prediction of sales and future growth is possible. 

Advantages of Data Visualization

Let’s see some of the major advantages and disadvantages of data visualization in detail.


  • Getting better agreements 

    The exhibition of two components or situations frequently must be analyzed in business. There is a conventional approach to examining both circumstances by analyzing their massive amounts of data. 

  • Superior methods 

    This method allows the representation of two perspectives in a graphical format. By utilizing Google patterns, we can understand information related to top projects or inquiries in a graphical form.  

  • Data sharing made simple 

    Organizations present another correspondence arrangement with the representation of the information. By sharing the visual data instead of the cumbersome information, the data can be more readily absorbed.  

  • Investigate precisely 

    Information perception helps to make patterns more obvious and to draw a better picture of the information. Thus, giving organizations a competitive edge.  

List of Data Visualization techniques and concepts 

Based on these factors, decide which data visualization technique you choose and how their features are configured. Let us discuss the different types of data visualization techniques and concepts applicable:


  • Charts

    Charts are the most convenient way to show the evolution of several data sets or one dataset in particular. In a pie chart, components of one whole are displayed as proportions between various elements over time.

  • Plots

    Two or more data sets are distributed across a 2D or 3D space using plots to illustrate their relationships with the parameters. There are also different types of plots. Among the most widely used visualizations are scatter plots and bubble plots. Analysts often use box plots to visualize relationships between large quantities of data.

  • Maps

    Many industries use maps as a means of displaying data. Using these tools, a geographical map, a building plan, a website layout, etc., can be located. Cartograms, dot distribution maps, and heat maps are the most popular types of map visualization.

  • Diagrams and matrices

    There are usually several data types in one diagram that illustrates complex relationships and links between them. Trees, hierarchies, and multidimensional structures can be used.

    When analyzing continuously updated data sets, matrix techniques are used to determine correlations.

Top tools for Data Visualization

Visualizing large data sets can be achieved with various tools, applications, and scripts. The features of most of them overlap, and they have a very basic design.


It is worth noting that some stand out when it comes to creating different types of visualizations or being considerably easier to use than other options.


  • Tableau

    A free public version of Tableau is available, as well as a desktop app, a server, and a hosted online version. There are hundreds of options for importing data from CSV files to Google Ads, Analytics, and Salesforce data.

    Multiple chart formats and mapping capabilities are available as output options. Designers can use color-coded maps to display geographic information in an easier-to-digest format than tables or charts.

  • Infogram

    Use drag-and-drop functionality to create social media posts, maps, infographics, dashboards, marketing reports, and more.

    A variety of export formats are available for finished visualizations, including .PNG, .JPG, .GIF, .PDF, and .HTML. Websites and applications can also incorporate interactive visualizations.

  • ChartBlocks

    The ChartBlocks API enables data import from “anywhere,” including live feeds. It is more difficult to import data into an app without automated modules or extensions than to switch to an app with automated modules and extensions.

  • D3.js

    Data manipulation with D3.js is possible thanks to this JavaScript library. While some apps allow non-programmers to use D3.js without knowing JS, the library requires at least some JS knowledge.

  • Google Charts  

    With Google Charts, you can create interactive charts for displaying online and embedding them into your website. Dynamic data and SVG and HTML5 output are used, so no plugins are required. Salesforce, Google Fusion Tables, and Google Spreadsheets are data sources.

    Using charts, you can scatter plots, bar charts, column charts, treemaps, timelines, gauges, histograms, tree maps, pie charts, area charts, timelines, and many other graphs. A simple CSS editor allows you to customize these charts efficiently.

What makes Data Visualization a key skill to have in 2024?

Examples from real-world applications demonstrate data visualization’s versatility: 


  • Correlation analysis. It is best to visually compare two variables to determine their relationship and correlation.
  • Examining the network. A network in this context is the entire audience of a particular market. Analyzers can recognize audience clusters by reading their network, including any influencing factors, links, and statistical outliers.
  • Analyzing changes over time. Using data visualization in this way is an essential function of data analysis. Data trends can be visualized and analyzed over time using visualizations.
  • Determination of frequency. Tracking changes is similar to frequency, but frequency measures how frequently a given event occurs.
  • Scheduling timelines. Project leaders can use Gantt charts to show how long each task will take within a project.

Also, many fields benefit from data visualization, including:


  • Research/Data Science: Scientists use data visualization to analyze complex data, recognize patterns, and understand datasets. To complete their tasks, data scientists can use various data visualization tools, including programming languages such as Python. The PG Certificate Program in Business Analytics and Data Science will show you how to accomplish this.
  • The financial sector: A data visualization tool can be useful for investors as it offers the performance of commodities, bonds, and stocks over time.
  • The healthcare industry: The choropleth map shows numeric values in different colors to illustrate other geographical areas. Medical professionals and epidemiologists can use them to track health hazards such as pandemics and heart disease.
  • The logistics industry: Data visualization helps shipping companies determine the best routes for shipping.
  • Politics: Campaign workers and pollsters can determine which regions voted for which candidates.
  • Applied Sciences: Scientists can better understand their experimental data with SciVis or Scientific Visualization.

Use cases of Data Visualization

Here are some of the example of use cases of data visualization:


  • Trends and spikes in data

    Trends or disruptions in the data that require both explanation and remedy are one of the most important tasks of sales and marketing professionals. Comparing this month’s blog traffic with last month’s is a simple use case.

  • Goals and results monitoring

    Monitoring progress toward a goal. A chart can be made that shows revenue performance against quarterly goals, the percentage of closed won deals, and progress on deal stages in the sales pipeline. 

    By monitoring deal performance, sales managers can identify why so many deals don’t close and take action to resolve them.

  • Notification of changes

    Sales managers are effectively informed. Managing sales pipelines may be useful for sales managers, who can receive notifications for new deals added by sales reps. The sales manager gets a notification via email. Agents can assist them in closing them by looking at these deals in the CRM.

    As society develops sounder strategies for tackling the commercial sector’s challenges, we increasingly rely on data. Data analysts are among the most in-demand professionals in today’s business world. Those specializing in IT data smithing are skilled at transforming mountains of information into actionable, useful information.

    Hero Vired can help you become successful in the world of analytics and data with the help of two unique programs meant to accelerate skills in the field.

    The PG Certificate Program in Business Analytics and Data Science associates a project-based learning approach with a unique curriculum and placement assurance. Offered in partnership with edX and Georgia Tech, the program combines 80+ live sessions and 7+ projects for a holistic learning experience. It also includes a chance to earn a MicroMasters credential from Georgia Tech upon successful completion of all relevant courses. 

    The Accelerator Program in Business Analytics and Data Science is meant for anyone looking to make a transition into the world of data. With 7+ projects and 70+ live sessions, the program offers guaranteed placement and is accredited by National Skill Development Corporation. With an innovative curriculum, the course imparts skills to succeed in an industry setting.

Data Visualization Examples

  1. Weather Maps – Weather maps are a common Data Visualization examples that is used to represent weather data in a simple and intuitive manner.
  2. Stock Market Graphs – Stock market graphs are another Data Visualization examples that is used to represent financial data in a way that is easy to understand. Data visualization in data science play a vital part in these. 

Why is it important for modern businesses and data professionals?

Let’s examine how data visualization can benefit businesses:


Large and complex data can be absorbed instantly


The information conveyed by large volumes of data can be grasped easily when presented effectively. Creating a visualization from text or numbers is much more difficult than processing ready images due to the series of neural processes required.


Visual representations also make it easier for us to understand the interrelationships between different data points. We internalize things more quickly once we see them.


Therefore, data visualization in data mining create small, easily stored ‘packages’ of information in the short-term memory of people who will be consuming it. 


These “chunks” are matched according to their hierarchy, color, line weight, scale, and placement. Viewers can understand the data’s significance and navigate easily using chunks.


Data-driven decision-making


Meetings with visual data tend to be shorter and more productive than ones held with only text or numbers. Stakeholders can gain a much deeper insight into patterns and trends by displaying data, allowing managers to reach decisions faster.


The benefits of data analytics can be achieved using visualization. Even though your sales staff may not be an expert at interpreting data, you can use the right data visualization to help them understand consumer behavior more deeply. 


With the right tools and training, data visualization allows you to become an expert in technical analytics and creative storytelling.


Engagement with the audience


A good visual presentation allows viewers to relate to data and engage more. The use of images can help drive opinions and actions because they produce emotional responses.


Data allows multiple stakeholders to understand and appreciate insights, communicate, and collaborate on projects. Data is now often presented visually in ways that are easy to understand. 



Data visualization tools and technologies are crucial to analyzing massive amounts of data and making data-driven decisions.


Among its many uses are:


  • Modeling complex events using data
  • Understand phenomena such as mathematical relationships, medical conditions, and weather patterns without having to observe them physically.

In conclusion, importance of data visualization is evident in data science, enabling businesses to draw valuable insights and make informed decisions. Both data visualization in data science are vital for a business. By using the right visualization techniques, businesses can quickly and efficiently analyze large amounts of data, identify patterns and trends, and communicate key insights to stakeholders.


Some best practices for creating effective data visualizations include choosing the right type, keeping it simple, and labeling accurately.
Some popular data visualization tools are:
  • Tableau
  • Power BI
  • D3.js
  • Google Charts
  • Infogram
When selecting the right data visualization technique, it is important to consider the type of data being represented, the audience, and the desired outcome. Factors such as the number of data points, the relationships between data points, and the level of detail required should also be considered.

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