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Importance of Data Visualization

Importance of Data Visualization

What is Data Visualization?

Data visualization is the use of visual elements like charts, graphs, and maps that provide a graphical representation of data. It is much easier to understand trends, outliers, and patterns in data by Data Visualization.One can perform Exploratory Data Analysis (EDA) in Data Science and Analytics projects.

Why Data Visualization?

  • Better and quick understanding of data: Oftentimes, when a data scientist is working on the project, some trends in the earlier stages are quickly identified by visualization. This process could be a lot more time-consuming if one only had to look at the data to understand it.
  • ‘A Picture Is Worth a Thousand Words’: It is much easier to convey your data projects to clients and stakeholders via graphs and diagrams than most other ways. It’s much easier to draw conclusions on the type of data, the underlying trends, and a lot more. It makes it easier for the human mind to comprehend pictures.
  • Finding Correlations in Data: One major aspect of Data Science projects is finding the relationships between independent variables. Data Visualization makes the process much easier.

Different types of Data Visualization:

1. Based on Amounts:

When the interest lies in the magnitude of some set of numbers.

2. Based on Distributions:

When you want to understand how a particular variable is distributed in a dataset.

3. Based on Proportions:

When you want to show how some group, entity, or amount breaks down into individual pieces that each represent a proportion of the whole.

4. Based on x-y relationships:

When many datasets contain two or more quantitative variables, and one may be interested in how these variables relate to each other.

5. Based on Geo-spatial data:

When datasets contain information linked to locations in the physical world.

Image Source: O’Reilly

How to Visualize Data?

Most Data Scientists and Analysts use Python or R programming language to code their visualizations. However, there are great tools that you can use to enhance your data capabilities and make useful dashboards.

Tableau is a famous tool used world-wide and provides great insights with data. It is easy to use and can be used to prepare, clean and format their data and then create data visualizations to obtain actionable outcomes that can be shared with other users.

Microsoft’s Power BI is another great tool that offers analytics tools, which can be used to analyze, aggregate, and share the data meaningfully.

Conclusion:

Data visualization is part art and part science. Accurately conveying the representation of data is the main purpose of data visualization. Good visual presentations enhance the message of the visualization. Every application has a different way of representing data, and one can make use of the different types of charts to choose one that best represents it. Data visualization can be used for storytelling and providing insights to decision makers, allowing them to act more quickly than if the data were presented as reports.