Data Visualization meaning and its importance:
The graphic representation of data or information over a graph, chart, or any visual form that relates to data with images.
The importance lies with the trends and patterns as this allows to be more easily seen without any difficulty. This is not limited to data scientists or data analysts, but serves an importance in any career.
Why data visualization?
Our human mind has a tendency to identify when a visual summary of the information is given in the form of charts and graphs because they make communicating data findings in an easier and conceptualized way.
Fig: Different forms of data visuals
How it is used?
- Swapping over time
Data changes with time-lapse. So, it is beneficial to see how data is trending over time.
- Frequency determination
Frequency deals with the time. So, it is better to examine the happenings over time.
- Determining relationships
It is essentially difficult to determine the relationship between two variables without a visual idea, but we need to be aware.
- Selection of a network
This talks about data analyzation. Most market professionals tend to analyze and interpret the data according to customer demands.
- Scheduling the risk and analyzed value
Things get confused for a complex data and tends to take more time than usual. So, it is advised to use color-coding formula.
Types of data visualization chart:
- Line chart
This illustrates changes over time containing two axes.
- Area chart
It is an extension of a line graph that displays graphically quantitative data.
- Bar chart
This represents a categorical data with rectangular bars with heights and lengths proportional to the values.
A representation of data in two axes that holds frequency over time.
- Scatter plot
It is used for finding correlations.
- Bubble chart
A 3D data display chart.
- Pie chart
A circular statistical graphic data.
It shows distance between intervals.
It shows a geographical representation.
10. Frame diagram
It is simply a problem pattern chart.
It is very crucial to the final step of data analysis. So, it must be adopted instead of conventional mode.