Data Science

5 Common Types of Data Visualization

In our past blogs, we have delved deeply into the nuances of topics like data science, machine learning, artificial intelligence, and big data. We have also highlighted their numerous technological procedures, methodologies, tactics, and working processes.

One component that was constant across all of these technologies was data. Data is regarded as the source of current technological innovation. We will discuss data visualisation as we continue with the event, the “data-centred” tool and technique.

What is Data Visualization?

By presenting the data in a visual framework, data visualisation is a technique that helps people understand the relevance of the data.

A viewer can quickly spot patterns, trends, and correlations in data when text-based data is shown in a data visualisation tool. Users can change the images used for data visualisation and extract data for thorough analysis thanks to their interactive and dynamic features. There are many different ways to display data besides the typical charts and graphs created in an Excel spreadsheet, including dials and gauges, maps of specific locations, infographics, heat maps, bar and pie charts, etc.

Suppose a data scientist wants to write sophisticated predictive analytics or machine learning algorithms. In that case, he will need to be able to view the findings to guide the results and make sure the algorithms function properly.

5 Types of Data Visualization

There is always a story hidden beneath the numbers we use to represent data or information, and when statistics are visualised, they take on a life of their own.

You may build trust with your audience by accurately presenting and displaying data. So, let’s look at how to choose the most sincere and endearing way to present data;

1.    Bar Graph

A bar graph is an ideal option with some characteristics or some careful recommendations if you want to examine data over time or the data is compiled in several categories, such as different industries, variety of foods, the success of a company over the previous five years, etc.

Bar graphs should be ordered chronologically, periods should be labelled on one axis, and quantities should be labelled on other axes to make them more effective and easy to read. Data should also not be arranged from most to least or least to most but in chronological order.

2.    Line Chart

Line graphs can also display data across time or categorise data like bar graphs do. The only distinction is that line graphs can be refined.

The line graph might be a good option if you wish to exhibit data over very long periods or continuously changing data.

The majority of the time, we draw nothing but a straight line because we don’t know how to complete data effectively in the period for which data is available.

3.    Pie Chart

It is a circular data visualisation presentation, sometimes called a circular chart. It is one of the most widely used types of data visualisation, but it is only effective when a thoughtful subset of the data adds up to the whole.

For instance, if 40% of the exam’s marks are considered passing, the pie chart can show that 40% of the exam’s overall marks are passing.

Circle charts cannot indicate a rise or decrease on their own, but we can convert percentages to proportions or proportions to percentages for this purpose.

4.    Quantagrams

Quantagrams are repeating pictograms or icons that depict quantities, such as

  • The number of individuals is a frequent example used to illustrate multi-character amounts using Quantagrams. You must have noticed Quantagrams at the restroom doors in the form of traditional male and female iconography. This strategy is appropriate for small numbers, small percentages, or small proportions.
  • Regarding pictograms, they are incredibly straightforward and come out as sound or reductive when applied to serious or extensive problems. If a serious problem is shown as a basic set of sorted icons, it will appear minimised. If vast statistics require the visualisation of data, typography is an option.

5.    Typography

It is not restricted to offering an outdated text-only answer; it is cleverly exploited to produce successful and useful content. It is limited to select circumstances where it can be acknowledged as the best solution provider.

Suppose the data is enormous or greater than 100, never represents a proportion of the entire or a rise or decrease in percentage, and cannot be compared to another number. In that case, it is suitable for typography.

A pictogram or icon that provides the viewer with a clear visual image in the context of the subject matter of data and figures can be coupled with typography for better visualisation.

Final Thoughts

This blog post has provided an overview of data visualisation, including examining its many elements, significance, and typical forms. In a word, data visualisation aims to make data digestible and understandable at a glance. Since data can be represented in various ways, attention should be given to selecting the most appropriate chart for visualisation.


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