What is data visualization? A definition, examples, and resources

Data visualization is a general term that describes any effort to help people understand the significance of data by placing it in a visual context. Patterns, trends and correlations that might go undetected in text-based data can be exposed and recognized easier with data visualization software.

Today’s data visualization tools go beyond the standard charts and graphs used in Microsoft Excel spreadsheets, displaying data in more sophisticated ways such as infographics, dials and gauges, geographic maps, sparklines, heat maps, and detailed bar, pie and fever charts. The images may include interactive capabilities, enabling users to manipulate them or drill into the data for querying and analysis. Indicators designed to alert users when data has been updated or predefined conditions occur can also be included.

Importance of data visualization

Data visualization has become the de facto standard for modern business intelligence (BI). The success of the two leading vendors in the BI space, Tableau and Qlik — both of which heavily emphasize visualization — has moved other vendors toward a more visual approach in their software. Virtually all BI software has strong data visualization functionality.

Data visualization tools have been important in democratizing data and analytics and making data-driven insights available to workers throughout an organization. They are typically easier to operate than traditional statistical analysis software or earlier versions of BI software. This has led to a rise in lines of business implementing data visualization tools on their own, without support from IT.

Data visualization software also plays an important role in big data and advanced analytics projects. As businesses accumulated massive troves of data during the early years of the big data trend, they needed a way to quickly and easily get an overview of their data. Visualization tools were a natural fit.

Visualization is central to advanced analytics for similar reasons. When a data scientist is writing advanced predictive analytics or machine learning algorithms, it becomes important to visualize the outputs to monitor results and ensure that models are performing as intended. This is because visualizations of complex algorithms are generally easier to interpret than numerical outputs.

The 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.

Common general types of data visualization:

  • Charts
  • Tables
  • Graphs
  • Maps
  • Infographics
  • Dashboards

More specific examples of methods to visualize data:

  • Area Chart
  • Bar Chart
  • Box-and-whisker Plots
  • Bubble Cloud
  • Bullet Graph
  • Cartogram
  • Circle View
  • Dot Distribution Map
  • Gantt Chart
  • Heat Map
  • Highlight Table
  • Histogram
  • Matrix
  • Network
  • Polar Area
  • Radial Tree
  • Scatter Plot (2D or 3D)
  • Streamgraph
  • Text Tables
  • Timeline
  • Treemap
  • Wedge Stack Graph
  • Word Cloud
  • And any mix-and-match combination in a dashboard!

Why data visualization is important for any career

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.

There are loads of free courses and paid training programs

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

Data Visualization certification course with Tableau, R and Python

ABOUT THE COURSE

Data science doesn’t need to be the complex subject it is thought to be. Not with Simpliv, for sure. Understand when to use the right tools, how to connect to the right data source, how to connect Tableau Desktop with R to optimize the functions of R, packages, libraries and saved models. Also learn to use Python scripts for your fields in Tableau in the same way as you do with R. Ideal for becoming a complete data professional.

Who is the target audience?

All the professionals who are passionate about business intelligence, data visualization, and data analytics.

Basic knowledge:

There are no prerequisites for taking up this certification training course.

 Curriculam

Visualization with Tableau

  • Tableau – An Introduction
  • Aggregation and Granularity
  • Action Filters and Action Highlights
  • Customer Segmentation Dashboard
  • Data Blending
  • Find top 10 Gems
  • Table Calculations (Explaining various Table calculations)
  • Difference between Table calculations and Calculated fields
  • LOD Functions
  • How to connect to databases (Video based)
  • How to integrate Tableau with R (Video based)
  • Dashboarding Techniques
  • Other Charts

Python and R basics

  • Tableau – An Introduction
  • Core programming concepts
  • Objects in Python
  • Visualizations in Python
  • Packages in Python
  • Packages in Python
  • Matrix Operations
  • Dataframes
  • Introduction to R
  • Objects in R
  • Core programming concepts
  • Visualizations in R (Packages in R)
  • Matrix Operations
  • Dataframes
  • Lists in R
  • Apply family functions in R
  • Projects and Tests

 Event Type – Online Classroom Training

Price:   $499 (Enroll Today and Get Flat 40% OFF($299) )

 New Batch starts from 23 Sep 2019 Days: Mon-Fri (15 Days) 09:00 AM – 12:00 PM (PDT)

Contact Us:

Simpliv

Email: support@simpliv.com

Phone India: 76760-08458

Phone USA: 510-849-6155

 

To Enroll Click Here: https://www.simpliv.com/virtual-classroom/data-visualization-with-tableau-r-and-python

 

 

What is Tableau? Why Tableau? How does Tableau work?

What is Tableau?

Tableau is a powerful and fastest growing data visualization tool used in the Business Intelligence Industry. It helps in simplifying raw data into the very easily understandable format.

Data analysis is very fast with Tableau and the visualizations created are in the form of dashboards and worksheets. The data that is created using Tableau can be understood by professional at any level in an organization. It even allows a non-technical user to create a customized dashboard.

The best feature Tableau are

  • Data Blending
  • Real time analysis
  • Collaboration of data

The great thing about Tableau software is that it doesn’t require any technical or any kind of programming skills to operate. The tool has garnered interest among the people from all sectors such as business, researchers, different industries, etc.

What is Tableau used for?

Usage of Tableau software are listed below:

  • Tableau software is used to translate queries into visualization.
  • It is also used for managing metadata.
  • Tableau software imports data of all the sizes and ranges.
  • For a non-technical user Tableau is life saver as it offers the facility to create “no-code” data queries.

As Tableau is an emerging technology, Data visualization tool is used for business intelligence industry. Organizations like Amazon, Walmart, Accenture, Lenovo and so on widely use this tool.

Why Tableau?

Why use Tableau software when there are a whole lot of tools available to perform data visualization?

Tableau is greatly used because data can be analyzed very quickly with Tableau. Also, visualizations are generated as dashboards and worksheets. Tableau allows us to create dashboards that provide actionable insights and drives the business forward. The products of Tableau always operate in virtualized environments when they are configured with the proper underlying operating system and hardware. Tableau is used to explore data with limitless visual analytics.

A few features of Tableau are given below:

  • Real-time analysis
  • Data Blending
  • Collaboration of data
  • No need any technical knowledge

How does Tableau work?

Tableau connects and extracts the data stored in various places. It can pull data from any platform imaginable. A simple database such as an excel, pdf, to a complex database like Oracle, a database in the cloud such as Amazon webs services, Microsoft Azure SQL database, Google Cloud SQL and various other data sources can be extracted by Tableau.

When Tableau is launched, ready data connectors are available which allows you to connect to any database. Depending on the version of Tableau that you have purchased the number of data connectors supported by Tableau will vary.

The pulled data can be either connected live or extracted to the Tableau’s data engine, Tableau Desktop. This is where the Data analyst, data engineer work with the data that was pulled up and develop visualizations. The created dashboards are shared with the users as a static file. The users who receive the dashboards views the file using Tableau Reader.

The data from the Tableau Desktop can be published to the Tableau server. This is an enterprise platform where collaboration, distribution, governance, security model, automation features are supported. With the Tableau server, the end users have a better experience in accessing the files from all locations be it a desktop, mobile or email.

Summary

  • Tableau is a powerful and fastest growing data visualization tool used in the Business Intelligence Industry
  • The Tableau Product Suite consists of 1) Tableau Desktop 2) Tableau Public 3) Tableau Online 4) Tableau Server and Tableau Reader
  • Tableau Desktop has a rich feature set and allows you to code and customize reports
  • In Tableau public, workbooks created cannot be saved locally, in turn, it should be saved to the Tableau’s public cloud which can be viewed and accessed by anyone
  • Tableau server is specifically used to share the workbooks, visualizations that are created in the Tableau Desktop application across the organization
  • Tableau online has all the similar functionalities of the Tableau Server, but the data is stored on servers hosted in the cloud which are maintained by the Tableau group.
  • Tableau Reader is a free tool which allows you to view the workbooks and visualizations created using Tableau Desktop or Tableau Public.
  • Tableau connects and extracts the data stored in various places. It can pull data from any platform imaginable.
  • The spreadsheet application used for manipulating the data while Tableau is a perfect visualization tool used for analysis

Check out the Simpliv Data Visualization certification course with Tableau to get a head start in your Business Intelligence career!

Related Articles

What is data visualization? Why data visualization is important for any career?

Data visualization is a general term that describes any effort to help people understand the significance of data by placing it in a visual context. Patterns, trends and correlations that might go undetected in text-based data can be exposed and recognized easier with data visualization software.

Today’s data visualization tools go beyond the standard charts and graphs used in Microsoft Excel spreadsheets, displaying data in more sophisticated ways such as infographics, dials and gauges, geographic maps, sparklines, heat maps, and detailed bar, pie and fever charts. The images may include interactive capabilities, enabling users to manipulate them or drill into the data for querying and analysis. Indicators designed to alert users when data has been updated or predefined conditions occur can also be included.

Why data visualization is important for any career

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.

Examples of data visualization in action


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. We’ve collected 10 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.

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. Plus, there are tons of great blogs and books about data visualization containing excellent examples, explanations, and information about best practices.

Importance of data visualization

Data visualization has become the de facto standard for modern business intelligence (BI). The success of the two leading vendors in the BI space, Tableau and Qlik — both of which heavily emphasize visualization — has moved other vendors toward a more visual approach in their software. Virtually all BI software has strong data visualization functionality.

Data visualization tools have been important in democratizing data and analytics and making data-driven insights available to workers throughout an organization. They are typically easier to operate than traditional statistical analysis software or earlier versions of BI software. This has led to a rise in lines of business implementing data visualization tools on their own, without support from IT.

Data visualization software also plays an important role in big data and advanced analyticsprojects. As businesses accumulated massive troves of data during the early years of the big data trend, they needed a way to quickly and easily get an overview of their data. Visualization tools were a natural fit.

Visualization is central to advanced analytics for similar reasons. When a data scientist is writing advanced predictive analytics or machine learning algorithms, it becomes important to visualize the outputs to monitor results and ensure that models are performing as intended. This is because visualizations of complex algorithms are generally easier to interpret than numerical outputs.

Continue Reading About data visualization

The 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.

Common general types of data visualization:

  • Charts
  • Tables
  • Graphs
  • Maps
  • Infographics
  • Dashboards

More specific examples of methods to visualize data:

  • Area Chart
  • Bar Chart
  • Box-and-whisker Plots
  • Bubble Cloud
  • Bullet Graph
  • Cartogram
  • Circle View
  • Dot Distribution Map
  • Gantt Chart
  • Heat Map
  • Highlight Table
  • Histogram
  • Matrix
  • Network
  • Polar Area
  • Radial Tree
  • Scatter Plot (2D or 3D)
  • Streamgraph
  • Text Tables
  • Timeline
  • Treemap
  • Wedge Stack Graph
  • Word Cloud
  • And any mix-and-match combination in a dashboard!

Learn more about data visualizations (and how to create your own)


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 are a perfect place to start

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 critique 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.

Learn about historical examples and theory from books

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.

There are loads of free courses and paid training programs

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

 

Data Visualization with Tableau, R and Python course

ABOUT THE COURSE

2 Data Visualization with Tableau, R and Python.jpg

Data science doesn’t need to be the complex subject it is thought to be. Not with Simpliv, for sure. Understand when to use the right tools, how to connect to the right data source, how to connect Tableau Desktop with R to optimize the functions of R, packages, libraries and saved models. Also learn to use Python scripts for your fields in Tableau in the same way as you do with R. Ideal for becoming a complete data professional.

Who is the target audience?

All the professionals who are passionate about business intelligence, data visualization, and data analytics.

 

Basic knowledge:

There are no prerequisites for taking up this certification training course.

 

Curriculam

Visualization with Tableau

  • Tableau – An Introduction
  • Aggregation and Granularity
  • Action Filters and Action Highlights
  • Customer Segmentation Dashboard
  • Data Blending
  • Find top 10 Gems
  • Table Calculations (Explaining various Table calculations )
  • Difference between Table calculations and Calculated fields
  • LOD Functions
  • How to connect to databases (Video based)
  • How to integrate Tableau with R (Video based)
  • Dashboarding Techniques
  • Other Charts

 

Python and R basics

  • Tableau – An Introduction
  • Core programming concepts
  • Objects in Python
  • Visualizations in Python
  • Packages in Python
  • Packages in Python
  • Matrix Operations
  • Dataframes
  • Introduction to R
  • Objects in R
  • Core programming concepts
  • Visualizations in R (Packages in R)
  • Matrix Operations
  • Dataframes
  • Lists in R
  • Apply family functions in R
  • Projects and Tests

 

Price:  ₹ 16665    ( Enroll Today and Get Flat 40% OFF )

New Batch starts from 21ST Jan 2019 Days: Mon-Fri (15 Days) 07:00 PM – 10:00 PM (IST)

 

Contact Us:

Simpliv Services India Pvt Ltd

Email: support@simpliv.com

Phone: 76760-08458

Email: sudheer@simpliv.com

Phone: 9538055093

 

To Enroll Click Here: https://www.simpliv.com/instructor-led-courses/data-visualization-with-tableau-r-and-python/?7-DEC-SEO