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Data Visualization in Data Science

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Brief Description: InData Science one of the “MOST IMPORTANT” skill is visualizing and understanding the trend,distribution and complexity of the data.

Data visualization is the act of taking information (data) and placing it into a visual context, such as a map or graph.Data visualizations make big and small data easier for the human brain to understand, and visualization also makes it easier to detect patterns, trends, and outliers in groups of data.

Data visualization is used mainly for data checking and cleaning, exploration and discovery, and communicating results to business stakeholders. Most of the data scientists pay little attention to graphs and focuses only on the numerical calculations which at times can be misleading.

Why is Data Visualization Important?
  • Data visualization is increasingly being seen as the essential last step of any successful data-driven analytics strategy.
  • When data scientists are in the middle of a complex project, they need a way to understand the data that’s being collected so that they can monitor and tweak their process to ensure it’s performing the way it should.
  • Data visualization also makes it easier to detect patterns, trends, and outliers in groups of data.
  • Leading the target audience to focus on business insights to discover areas that require attention.
  • Revealing previously unnoticed key points about the data sources to help decision makers compose data analysis reports.
  • It helps provide the stakeholders and other team members with quality information by transforming huge amount of data into easily understandable pictures and graphics.
Benefits of Data Visualization-

Considering the impact that data has in the growth of the business, here are a few benefits

  • Helps to identify the latest trend to improve the product and increase the profits for business.
  • Data visualizations make big and small data easier for the human brain to understand,leading to better analysis.
  • Helps understanding the story-The human brain is not able to understand or even just imagine large amounts of numbers or text at once. It needs a visual representation to make sense of them and consequently translate raw data into tangible concepts.
  • Data Checking and Cleaning
  • Data Distribution
  • Dimension Reduction – to understand various dimensions of data