Four Ways to Apply Data Visualisation to Your Daily Life

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Data is present everywhere. We are surrounded by different forms of data, be it Google Maps or diet planners. One of the major reasons for the widespread use of data is the ease of communication. Visuals communicate more easily and effectively than words. Medieval paintings are a testament to the fact that people have used data visualisation since time immemorial for conveying complex ideas.

Keep reading to understand the meaning and logic behind data visualisation.

What is Data Visualisation?

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Raw, written data is available in both organised and unorganised forms. Using data visualisation, it is translated into visible forms like graphs, pie charts, videos, GIFs, reports and images. You do not have to go through large chunks of data. You can quickly understand the trends in the data through visuals. Companies use these techniques for the process of decision-making.

Four ways to apply data visualisation in real life

Raw data is of no use to a company. The primary aim of processing data and generating visuals is to easily identify the patterns in big data. Companies all around the world are seeking individuals with knowledge of big data and its applications. Let us understand how to perform the visualisation of big data in the real world.

Bar Charts: Bar charts are one of the best visualisation options for comparing two or more quantities. You can use it to analyse your income and spending in different months of the year. Companies can use this tool for measuring the performance of their internal departments against each other.

Mapping: Maps are used for understanding the distribution of resources or products for a company. You can use a map for planning the itinerary of your international or local trips. If a company has operations overseas, a map helps in comparing the performance of different branches against that of the headquarters.

Matrix: A matrix is a visual representation that displays the existence of several factors (or groups) in a general location. If you have a big data set, a matrix helps in understanding the relationship between different elements of this data set. You can use a matrix for identifying the cause of a problem. Thus, you can allocate resources according to the needs of different areas.

Box Plots: A box plot helps in understanding the range of a data set and several other characteristics of a given group. From this representation, you can understand the outliers, range and variance of a particular data group. It can then be compared with others. Using this representation, you can check if there are any unused groups in a data set.

Is data visualisation scientific?

Data analytics (or visualisation) is much more than turning written data into visuals. The process of accomplishing this goal has a lot of scientific reasons. They show how humans gather and process any type of information.

According to several psychologists, the human thinking process can be generally categorised into two systems. System one is responsible for reflex thoughts. These refer to the ideas or solutions that the brain gives without processing any information. On the contrary, system two generates thoughts and solutions based on the results obtained after processing the information received from outside.

When system one receives data in a familiar format, it becomes easier for system two to process it more efficiently. System one is used to immediately process the information received from our visual senses. Thus, it is better if data is represented visually to allow better decision-making.

Start a Data Analytics Course with Placement

Imarticus Learning offers PG in Data Analytics and Machine Learning. This course can be helpful for aspiring data scientists. It guarantees placement opportunities in renowned companies. These include HSBC, Standard Chartered, ICICI Bank, Infosys, Wipro, HDFC Bank, Deloitte, Oracle, and many more. The curriculum is designed by professionals to include the latest job-relevant skills using tools, packages and programming languages. They include Python, Spyder, pandas, NumPy, OpenCV, Colab and TensorFlow.

This course demands the completion of 25 projects. These will help you understand concepts better and improve your job prospects. These real-world projects are completed in class. You can add these to your resume as a part of your portfolio. Additionally, you can be a part of competitions like Hackathon. You can also participate in mock interviews to improve your skills. The professionals at the institute will help you create LinkedIn and GitHub profiles to attract employers.

Learn data analytics by visiting the website. You can interact with professionals for solving your queries through call or chat support on the website. You can also visit one of our centres.

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