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Data Visualization in Data Science

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It is a very challenging task to understand something that is not clear or mixed with many elements. We know the things better when intended information is clear and easy to understand. The same thing applies to data as well. We all know how data has gained popularity these days. 


Organizations across the globe largely depend on the data to make more informed and data-driven decisions. Data is getting generated at an alarming rate and to process these extensive datasets, we need advanced data processing technologies like Hadoop. The process does not end with merely processing these vast amounts of data. After processing the data, it just turns out to be structured data or data which has a sequence. 


To get the hidden insights or valuable information or to understand the data we need a clear explanation, and that is possible with the help of data visualization.  Let’s discuss what data visualization is all about. 



What is data visualization?


Data visualization is a process of representing data in a graphical format by using different visual elements such as charts, tables, graphs, maps, infographics, etc. There are various data visualization tools available in the market to represent the overview of the data in a user/customer understandable format. Visualization tools depict the trends, outliers, and patterns in data. 


Data visualization tools are very essential for big data-related technologies. They help us in identifying and spotting the trends in data and thereby in taking data driven decisions.


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Need for data visualization?


Many companies across the globe are leveraging maximum benefits out of data with the help of data visualization tools. Visualization helps the organizations in analysing data analytics and in finding opportunities and threats. There are organizations like government-owned companies, hospitals, and financial institutions investing a lot in data visualization to analyse customer spending, needs, opportunities, etc.   


Below mentioned are some of the causes to consider for the data visualization.


  • It constructs the way to absorb the right information 
  • Visualises the  relationships and data trends  
  • Enables the organizations in acting on emerging trends faster than others. 
  • It depicts the tastes and preferences of people belonging to specific geography. 

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How does data visualisation work?


The process of data visualization is associated with different technical aspects and involves different people like graphic designers and statisticians. There must be a proper collaboration between these people to execute the visualization process. 


Below steps depict how data is visualised.   


  • Acquire data:The first thing one to do is to get the datasource from a network or a file on a disk. 
  • Categorize the data: Provide some structure to the data for the understandable purpose and divide it into categories.      
  • Filter: This stage involves filtering the data, which means eliminating unnecessary information from the data.  
  • Mine: Use statistical models or data mining models to divide the patterns or present the data in a mathematical context. 
  • Represent: In this stage, you are required to select a primary visual mode such as bar graph, list or tree, etc. 
  • Refine: Add some colours and visual effects to make it more colourful and bright. 
  • Interact:  Add methods for controlling the data and features visible for the visualization purpose.  

[Related Page: Using Hadoop for Data Science]


The following are the commonly used data visualization formats. 


  • Charts
  • Tables
  • Maps
  • Infographics
  • Dashboards

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Types of data visualizations


Charts


A chart is defined as a graphical format used to explain the data in an easily understandable way. Human brain understands images quickly compared to text. 

There are four commonly used charts, and they are,


  • Pie chart 
  • Bar chart 
  • Line chart 
  • Histogram

Let's discuss each one in detail. 


Pie chart: 

The pie chart is widely used for data visualization purposes. It divides a circle into proportional segments to give a complete overview of data. Each portion of the chart contains specific information. The pie chart is often designed by considering total data equal to 100% and divides the data into specific segments. The pie chart is best suitable for data that contains a few segments. If data contains more segments, then the pie chart has to be sliced into many more sections.


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Bar chart:

A bar chart or a bar graph is also used for a pictorial representation of data. It is used to represent the categorical data with the help of rectangular bars, and each bar is proportionate to the value it holds. 


One axis of the bar is used to specify the categories to compare and the other axis is used to measure the values. The below image shows how exactly a bar graph is:


Line chart: 

A line chart or line graph is one of the commonly used graphs for presenting the data in a visual format. It is similar to the bar graph. In the line chart, information is displayed as small dots called ‘markers’ and are joined with the help of a line. A line chart is commonly used to spot the trends in data over a period of time. 


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Histogram:

The histogram looks similar to the Bar chart, but the main difference between these two is a bar chart is used to compare two variables whereas histogram is related to only one variable. It is used mainly applied to the underlying frequency distribution of continuous data. 


Tables


The table is used for comparing the categories of different products. In this process, the items which we take into consideration for comparing will be placed on column sections, and the categorical objects are placed on rows. The quantitative values are set at the intersection of the row and column, and it is called a cell.  


The below-shown table would give you some sort of idea on how a tabular representation of data helps in data visualization. The below table shows the details of the payment for buying or leasing of different Cars. In the below table the first two columns are being compared about payment amounts and the other two columns contain additional information.  


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Infographics


Infographic will enable you to turn the boring information into a compelling image that could be understandable by anyone. Interactive infographics play an essential role in a clear understanding of what you intend to say.  Organizations across the world are using infographics as a central element to reach the targeted audience with a clear message on it.  


Latest Data visualization trends.


Data has become one of the hottest topics in today's business world.  Organizations have always been in search of insights out of data and to make them useful for the overall development of their organizations. The below mentioned are the five trends that had affected the data positively. 


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Data beyond the visuals:


Visualization is a fundamental concept, but understanding the core concept behind the visuals is more important. Many people often stuck at the visual effects, and they are in confusion about how to incorporate those insights they have gotten out of data.  Beyond the visuals, data should empower the teams to take actionable decisions. 


The latest trends in data visualization are empowering team's to know even how technology works and its capabilities. The past data visualization tools are only capable of delivering its services on desktops or laptops but not on the mobile devices, and the scenario has changed due to recent developments. High capable data visualization tools are enabling the organizations to utilize the 100% of data. 


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Data democratization.


It means the data democratization has been happening in each field ranging from small organizations to larger ones. Data democratization is nothing but free accessibility to data ranging from lower level employees to top authorities to take instant and accurate decisions that would positively affect the business. 


Experts say that “the main intention behind developing data democracy is to remove the barriers to access or understand the data.” -Bernard Marr


In recent years the usability of data has been increased by all the departments for accomplishing their day to day tasks. Enabling data access for each department has become essential, and it helps them in taking required and valid decisions.  All the department can utilize the data to optimize the opportunities and thereby to increase the profits. But the problem is all data visualizations are not yet ready to support the Data democratization.  


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Storytelling with data:


Human beings are very likely to understand the data in a story format and remember for longer times. With the help of visualizations, you can create the stories that can deliver efficient, personalized and experienceable stories for its members.  Embedding visualizations right into the applications and portals are helping organizations for focussing on storytelling with data.    


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Data cleansing:


Data gives effective results if it is clear and contains full-fledged information. Analysing the data that contains a lot of miss information could lead to misleading of the complete information. So to tackle this problem organizations have started employing the human resources in fulfilling the data gaps. In 2019 organizations have started investing in data cleansing to eradicate the missing data problem. 


[Related Page: Overview of Data Modeling]


Data governance:


As the data popularity has been increasing over a period of time, there must be a secure system to protect this valuable resource. To ensure data security companies started making policies to ensure data safety, it is nothing but Data governance.  Data governance contains a set of rules such as permissions, common terminology, rules for use, communication strategy, security planning, etc., are covered under this. 


Conclusion


Using data visualization for presenting data would help in spotting trends and thereby improving the overall performance. Being aware of the trends could help in identifying the opportunities and to escape from the negative trends. Visualization simplifies the data and converts that into an easily understandable image format that can be remembered easily for a longer time than the text. 


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Ravindra Savaram
About The Author

Ravindra Savaram is a Content Lead at Mindmajix.com. His passion lies in writing articles on the most popular IT platforms including Machine learning, DevOps, Data Science, Artificial Intelligence, RPA, Deep Learning, and so on. You can stay up to date on all these technologies by following him on LinkedIn and Twitter.


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