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What Is Data Visualization?

Data visualization is the technique of converting raw data into a visual representation , such as a map or graph, to make data easier for us to understand and extract useful insights .

The main goal of data visualization is to put large datasets into visual representation . It is one of the important steps and simple steps when it comes to data science

Selecting Best Data Visualization Techniques

Picking out best techniques solely depends upon the type of visualization we want to achieve between various data sets, such as:

1 Relationship

2 Distribution

3 Composition

4 Comparison

1 Relationship

If our main goal is to show a relationship between between two or more sets of variables , the best choices would be:

• Bubble Plot ( in case of three variables/dimensions)

• Scatter Plot ( in case of two variables/dimensions)

2 Distribution

If our main goal is to show the distribution of the data points , the best choices would be:

• Histogram/Column Chart( in case of few data points )

• Line Histogram ( in case of large number of data points)

• Scatter plot ( in case of two variables)

3 Composition

If our aim is to highlight the composition of our data, having following properties:

a. Dynamic Nature Of composition:

Best Choices would be:

• Stacked Column Chart

• Stacked Area Chart

b. Static Nature Of composition:

Best Choices would be:

• Pie Chart

• Waterfall Chart

4 Comparison

If our main goal is to show a direct comparison between two or more sets of data points / information in following way:

a. Comparison on the basis of Multiple items

Best Choices would be:

• Spider charts( In case of Less Items)

• Bar charts( In case of More Items)

• Table

b. Comparison Over Time

Best Choices would be:

• Line Chart

• Column Chart( just like we used for distribution as mentioned above)

• Area Chart

Summing up all the above mentioned topics

With this I finish this blog

Thank you so much for taking your precious time to read this blog. Feel free to point out any mistake(I’m a learner after all) and provide respective feedback or leave a comment.

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