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POWER OF DATA VISUALIZATION IN DATA SCIENCE

If someone out there is thinking about the way through which boring collections of data, confusing sentences, and multiple numbers can all be turned into wonderful designs, then the answer is yes and that is what we do with the help of visualization.

Thanks to rising technologies and an increasing number of people like us using it, which produce a very large amount of data through almost every activity we are doing nowadays. Every online purchase and every single post or comment on social media, every account with all our details generates data. So, Data Science helps to study and analyze such data to make some future predictions.


Data visualization is the visual representation of data where the huge data is represented with the help of different graphs, charts, and maps. By using such visual tools (graphs, charts, and maps), the data becomes easier to understand and remember. It is easy to study and explore the data with the help of graphs and charts than just seeing a bundle of data.


It is always difficult to scan a large amount of unfiltered, scattered data or numerical data like large excel sheets, word documents, or tables. But if the same data is well analyzed and organized properly it becomes easy to study and gain some insights through that data. Such data allow a more efficient transfer of knowledge among people. Hence, to get the best out of the available information and use it for future business growth or further estimates, data visualization is important.


For example, suppose someone asks you to examine a spreadsheet or a list of figures by eyes. It could be a very brainstorming, confusing, and irritating task for almost every one of us. And after all this still, there will be a big question mark on whether we can reach any correct conclusion through such data? It takes longer to study such data and come to any conclusion. On the other hand, appealing visuals have the power to say many things through small graphical representations, and hence they also save a lot of time.

This is the reason why data visualization is important and powerful. Visualization can turn such tedious data into visual information that people can understand. Interactive maps, charts, and graphs are powerful tools in visualization. They share quantifiable information without confusion. When information is plotted in the form of graphs certainly it becomes easy to compare things just by looking at them which in turn helps to find the correlation between different objects.


As humans, we are all brought up by listening to stories. Everyone among us remembers at least one story from our childhood. So, it’s our natural tendency that we not only remember stories, but we understand them very easily. While working with large datasets, visualizations help us to create and tell stories about the data in our hands by creating different visuals for that, so people like as well as digest it with minimum effort.


Example: Following is one small example of visualization with a bar chart. This shows the profit an organization has received in different categories. Just by looking at the chart, anyone can easily tell which category has the highest profit and which one has the lowest. Multiple business decisions can be made with the help of such visualization. As mentioned above, the following visualization is easy to read and understand, it’s informative, helpful in decision making, it saves time of looking into all the records or checking the profit data tables, and finally, it looks very neat and organized and it can be the best way to transfer the information.

All in all, we can say there is a good relationship between data visualization and data science because data science is learning more and more about the data to get insights through it and this learning process is made easy with data visualization. So, we can say data visualization is a subgroup of data science or it is a technique used by the data scientists to analyze the data and conclude some results. Data science can be a prediction, recommendation, classification, or categorization of data to get some insights through it, and data visualization works as a key component in all this.

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