Last Updated on by Kumar Raja

The Data Visualization Process In Data Science

Naturally, the human brain finds it quite challenging to understand & process information that isn’t clear or mixed with many elements. If things are well organized, then it becomes easier for the brain to understand & process the information. The same logic applies to data.

The world has now become more data-centric & the growing prominence of Big Data across every major sector is well known to everyone. Business stakeholders these days rely on data-driven insights to make strategic decisions related to their business. However, it is a bit difficult for the stakeholders to understanding & analyzes the insights that are extracted from Big Data, let alone make decisions based on it. 

So, it’s the duty of Data Scientists to present the insights from data in a understandable format with attractive visuals by making use of advanced data visualizing tools & this process is nothing but Data Visualization. If you are interested in getting a clear idea about the data analyzing technique in Data Science, then join us for the Best Data Science Training In Hyderabad program offered by Kelly Technologies.

Now, in this post, let’s discuss what data visualization is all about.

What Is Data Visualization?

Data Visualization can be interpreted as the process of representing the insights extracted from data in a graphical format in the form of attractive visuals like charts, tables, graphs, maps, infographics, etc. Data Visualization is an essential task in the skill inventory of a Data Scientist & they can make use of different Data Visualization tools like Tableau & others to visually depict the trends, outliers, and patterns in data.

Data Visualization technique helps in pinpointing the trends in data, and thereby it improves the stakeholders in better understanding them to make valuable data-driven decisions.

Data Visualization Steps-

  • Acquire Data

The very step is to find the source from where you can extract or mine the relevant data.

  • Categorize The Data

The collected data is then categorized & proper structure is given to it.

  • Filter

In this step, data is filtered by eliminating any unrelated or misinformed information. 

  • Mine

In this step, data is subjected to advanced statistical & data modelling techniques to analyze the patterns.

  • Represent

In this step, the relevant visual mode, such as bar graph, list or tree, etc. is selected.

  • Refine

The visuals are then refined with the help of creative dashboards from the data visualization tools.

Tableau is one of the most widely used tools concerning the Data Visualization process in Data Science. Work towards mastering the art of Data Visualization & other data analytic techniques in Data Science by joining for the real-time Data Science training program offered by Kelly Technologies.

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