a quick start guide to data visualization
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Data visualization refers to the visual depiction of information and data. It uses graphic elements to provide an easy way to understand trends and patterns in data.

Today, data visualization tools are essential to analyze massive amounts of information. These tools can help users make data-driven decisions.

This article outlines the importance of data visualization and its techniques. After reading, you will have basic knowledge of data visualization and how to use it.

Importance of Data Visualization

Humans are highly visual creatures. Colors and patterns attract our eyes and attention.

This means that data visualization can help people understand data better. It grabs our interest and keeps our eyes on relevant data.

Using a pie or a bar chart allows you to see trends and patterns. This approach can be more effective than looking at a massive spreadsheet of data.

Below are some of the benefits of data visualization:

  • It allows users to explore data with presentable results.
  • It streamlines the pre-processing stage of the data mining process.
  • It supports the data cleaning process and detects incorrect and missing values.
  • It combines categories to reduce the amount of data.

Data Visualization Techniques

There are several ways to visualize massive amounts of data. Today, there are various data visualization tools to support data analysis.

These tools can form different types of visual elements from vast amounts of data. Below are some data visualization techniques that you can use to interpret data.

Charts

There are several kinds of charts or graphs that can help you visualize data. The best chart for you will depend on what kind of data you have on hand.

The most common examples of data visualization charts include:

  • Line chart
  • Pie chart
  • Bar chart
  • Scatter chart
  • Bubble chart
  • Timeline chart

Box Plots

A box plot gives users a standardized way of displaying distributed data. Generally, the data in the graph has a five-number summary:

  • Minimum
  • The first quartile (Q1)
  • Median
  • Third quartile (Q3)
  • Maximum

The box plot shows outliers and what the values of the outliers are. It also shows if the data is symmetrical and if your information is inaccurate.

The graph gives users a good sign of how spread out the values in the data are.

One benefit of using a box plot graph is that it takes up less space than other graphs. This advantage can be helpful when comparing distributions between groups or datasets.

Histograms

Unlike other charts, histograms show the distribution of data over a certain period. These visualizations can help establish the concentration of values.

Histograms also identify exceptional values or gaps in the datasets.

Histograms are useful for showing the frequency of a specific occurrence. For example, you want to see the click-through rate of your website over the last week. A histogram can help you in this effort.

A histogram will show you which days your website saw the highest and lowest number of clicks.

Heat Maps

A heat map uses colors the way a bar graph uses width and height to visualize data.

You can use a heat map if you want to see which areas of a webpage that gets the most attention.

A heat map visualizes data so information can be easy to assimilate and make decisions from.

Heat maps are useful for two purposes: visualizing correlation tables and missing values. In both cases, the map conveys the data in a two-dimensional table.

It is important to note that heat maps are not replacements for more precise graphs, such as bar charts.

Tree Maps

A treemap displays organized data as a set of nested rectangles. This map displays the data based on a hierarchy. As such, it comes with parent elements grouped with their child elements.

The sizes and colors of the rectangles are proportional to the values of the data they represent.

Treemaps make efficient use of space. With this graph, you can display thousands of items on your screen.

Word Cloud/Network Diagram

Not all data are the same. Because of this, unstructured and semistructured data need different visualization techniques.

A word cloud shows a word’s frequency in a body of text with its relative size in the cloud. Researchers use the word cloud on unstructured data to identify the frequency words.

The network diagram is another visualization technique for semi-structured or unstructured data.

The diagrams represent relationships as two components: nodes and ties.

Nodes refer to the individual actors in the network. Meanwhile, ties are the relationships between the actors.

Researchers use the network diagram for many applications. They help analyze social networks and map product sales across geographic areas.

These are some of the data visualization techniques to represent data. The methods allow you to understand data better, regardless of the amount of data on hand.

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