# Data Types and Their Impact on Choosing the Right Type of Data Visualization # Data Types and Their Impact on Choosing the Right Type of Data Visualization

Choosing the right type of data visualization depends on several factors, of which one of the most important is the type of data to be plotted.

Data types dictate which data visualization can or cannot to be used, and this becomes quite evident when working with data visualization tools such as Tableau. Often times your data visualization would look very different if your variable is set to discrete vs continuous.

So, let’s spend some time learning the different data types first. Image Source: LDA (Learn Data Analytics) – www.LearnDataAnalytics.ca

## Data Types

Data can be divided into two classes: 1) Qualitative or 2) Quantitative.

Quantitative data answer the what question?

• What is the name of the student? John Smith
• What is the name of the city? Toronto
• What is the colour of your eyes? Black

Here, John Smith, Toronto and Black are all qualitative/categorical data values.

Quantitative data on the other hand answer the questions such as how much, how often, and how many?

How many students are in this class? 10

How many days are there in a week? 7

How much rainfall is expected this week? 15cm

Here, 10, 7, and 17cm are all quantitative/numerical data types.

Qualitative data can be further broken down into 3 categories.

• Nominal data is just a list of values with no order to it. For example a list of colours, red, blue, green or a list of names or cities etc.
• Ordinal data is also a list except the list for ordinal data has a sequence and order to it. For instance, small, medium, and large. In ordinal data, the order matters.
• Binary is when the data can only have one of the two values. 0 or 1, True or False, Yes or No etc.

Quantitative data can be further classified as Discrete or Continuous.

• Discrete numerical data can only be considered as a whole number. For example, there can be 2 students in a class or 3 students. There cannot be 2.34 students in a class.
• Continuous numerical data can take decimal values. For example, the weight of a package can be 2.34 kg rounded to 2 decimal places and 2.3124 kg rounded to 4 decimal places. The weight can take any value between 2 and 3.

Choosing the Right Type of Data Visualization

Choosing the right type of data visualization is done always keeping in mind the type of data being plotted.

For example, if you wanted to show the sales per region, then a bar chart would be the best choice.

The chart below shows a categorical variable (region) plotted against a numerical variable (sales). However, if you want to show the sales trend over the past 4 years, then a line chart would be the best option. The chart below shows a numerical variable (sales) plotted against a categorical variable (date). If two numerical values are being plotted, then a scatter plot would be the best option. The chart below shows average sales plotted against average quantity for all product sub-categories. Want to learn more of these tips and tricks, especially with an experienced industry-leading instructor in a live session? Check out the beginner-friendly Data Analytics Courses at LDA (Learn Data Analytics) – Beginner-Friendly Data Analytics Courses, Hands-on Learning, Live & Online Classrooms, Led by Industry Experts! 1800-400-5321 | info@learndataanalytics.ca | www.learndataanalytics.ca

Data Types and Their Impact on Choosing the Right Type of Data Visualization
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