The tendency of human eyes is to get attracted more towards visuals rather than written content. You may also have faced this situation where you felt easier to understand through visuals like charts, graphs, etc.
Thus, data visualization comes in handy as it organizes the raw data into an easier visual format. It can help by providing data in the very efficient possible way.
Data visualization gives a fast and productive way to convey the message in a widespread way by using visual information.
Uses of data visualization:
Data visualization is used in almost all industries to improve sales with existing customers and also target new markets and demographics for possible customers.
The major use is the preprocessing part of the data mining procedure.
It is an influential way to analyze data with presentable outcomes.
It plays a role in mixing sectors as part of the data reduction process.
It helps the process of data cleaning by locating inaccurate and missing values.
Simply, we can say that data visualization gives a clear idea about what raw information means through visuals in a way that is universal, and effective.
There are different types of techniques you can use to visualize data that will be discussed in the blog.
A box plot or box and whisker plot give a visual outline of information through its quartiles.
Box Plot Visualization
In simple language, we can understand that box plots indicate the five-number summary of a set of data which comprises the minimum score, lower quartile, median, upper quartile, and maximum score.
(Recommended blog: 4 Types of Data Visualization Using R Programming )
A histogram is a graphical presentation of information using bars of various heights and in a histogram, each bar groups numbers into ranges.
Sample Histogram Graph
Heat Maps
A heatmap has a very different concept of representing the data. It is a graphical portrayal of data that uses different colors to address different values.
This difference in color representation makes it easy for the viewers to understand the trend more quickly.
It is beneficial for two major purposes:
In both cases, the information is communicated in a two-dimensional table.
For instance, if you need to dissect which time of day a store makes the most deals, in that case, you can use a heat map that indicates the day of the week on the vertical axis and time of day on the horizontal axis.
After that, by shading in the matrix with colors that relate to the number of deals at each time of the day, you can specify the trends in the data that enable you to decide the specific times your store experiences the most deals.
There are several types of charts:
It is one of the simple techniques of data visualization. These types of charts are used to compare the quantities of different categories.
Hence, values of a category are addressed with the aid of bars and they can be designed with vertical or flat bars, with the length or height of each bar addressing the value.
If you want to examine data over time or the data is assembled in multiple sectors like different industries, variety of food, etc, a Bar Graph is the best option with some characteristics or some sorts of thorough ideas.
Bar Charts
It is used to plot the relationship of dependence of one variable on another like if you want to show data over very long periods or continuously changing data, the line graph could be a solid option to consider.
To plot the connection between the two variables, we can basically call the plot function.
The line chart is most often used to indicate trends and evaluate how the data has changed over time.
Line Charts
Pie Chart is one of the very basic and well-known techniques of data visualization. It is very simple and easy to understand.
It is a circular statistical graph that supposes pieces to clarify numerical ratios.
Thus, here the arc size of each piece is equal to the amount it indicates.
For example, a company witnessed a growth of 150% in which they found out 60% of growth was due to marketing, 40% was due to sales, 30% was due to product and 20% was due to technology adoption.
Pie Charts
It is a two-dimensional plot denoting the joint variation of two data elements.
It is a two-dimensional plot denoting the joint variation of two data elements.
Simply, it is a type of mathematical illustration that shows the value for generally two variables for a set of data by using Cartesian coordinates.
Scatter Charts
Bubble charts are a variation of scatter charts in which the data points are replaced with bubbles.
Also, an extra proportion of data is portrayed in the size of the bubbles. You can use this chart for analyzing patterns or correlations.
Each dot in a bubble chart adapts with a single data point. The variables’ values for each point are implied by horizontal position, vertical position, and dot size.
Sample Bubble Charts
This method indicates hierarchical data in a nested format.
Sample Treemaps
The assortment of big data brings difficulties because semistructured and unstructured information requires new visualization techniques.
Word Cloud
A word cloud visual addresses the frequency of a word inside a collection of text with its general size in the cloud.
Another visualization technique that can be used for semistructured or unstructured information is the network diagram.
Network Diagram
( Also read: Advanced Data Visualizations in R Programming )
Wedge stack graphs are one of the techniques of data visualization that shows hierarchical data in a radial system.
Wedge stack graphs
A correlation matrix enables quickly recognizable proof of connections between variables by joining enormous information and quick reaction times.
Correlation matrices
A streamgraph is a variety of stacked area charts. Rather than plotting values against a customary y-axis, the streamgraph balances the baseline of each "stack" to make it even around the x-axis.
Streamgraphs
Dendrograms show the hierarchical connection between the objects. The major use of a dendrogram is to figure out the best path to allocate objects to clusters.
Sample picture of Dendrograms
Two types of dendrogram exist which results in 2 types of the dataset:
( Also read: Power BI and Tableau: Data Visualization Tools)
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