Understanding Data Visualization And Its Importance In Data Science Projects

One of the steps in the data science process is data visualization, which states that after data has been gathered,

Overview of Data Visualization

 

One of the steps in the data science process is data visualization, which states that after data has been gathered, processed, and modeled, it must be visualized to draw conclusions. When it comes to delivering and locating, manipulating and formatting data, data presentation architecture (DPA) is an important component of data visualization. 

 

The process of converting information/data into a visual context, such as a map or graph, to make data simpler to interpret and extract insights from is called Data visualization. The primary goal of data visualization is to make identifying patterns, trends, and outliers in large data set easier.

Why is Data Visualization Important?

 

Using visual data, data visualization offers a quick and efficient way to convey information to all audiences. Additionally, the practice can assist businesses in determining the variables that influence consumer behavior, identifying areas that require improvement or additional attention, making data more memorable for stakeholders, figuring out the best times and locations to sell particular products, and forecasting sales volumes.

Data Visualization Benefits:

 

  1. The capacity to process information quickly, gain a better understanding and act more quickly.
  2. A better understanding of the actions that need to be taken to improve the organization; 
  3. A better ability to keep the audience interested with the information they can understand; 
  4. An easy way to distribute information that increases the opportunity to share insights with all parties involved; the need for data scientists is eliminated because data is more easily accessible and understandable.
  5. A better capacity to act on findings quickly and, therefore, achieve

 

Example of Data visualization

 

The most popular visualization method, in the beginning, was turning data into a table, bar graph, or pie chart using a Microsoft Excel spreadsheet. Although these visualization techniques are still frequently employed, more sophisticated ones are now also available, such as the following:

 

  • Infographics
  • Inflated clouds
  • Bulleted graphs
  • A heat map
  • Charting fever
  • Charts of time series

 

Summary:

 

A good data visualization should use graphics to communicate a data set understandably and effectively. The most effective visualizations make it simple to understand data quickly. They take complex information and simplify it so the target audience can easily understand it and use it as the basis for their decisions.

 

Want to start your career as a data scientist or data analyst? Learnbay offers the best data science course in Pune, and students can engage in real-world projects designed by industry professionals.