Data Science is a field that deals primarily with data. This means that any data science enthusiast should have a basic understanding of the different types of data in data science.
This article lists out most data types along with a brief description of them.
What is data?
Data is any kind of facts and statistics collected together for reference or analysis. The singular form of data is a datum.
What are the different types of data in data science?
Data can be categorized based on its characteristics as shown in the chart below.
1. Categorical or qualitative data
Categorical or qualitative data is data that is based on descriptive information such as “blue sky’, ‘white wall’, etc.
Categorical data can be further categorized into three other types of data in data science:
- Binomial Data – Binomial Data is the data with only two options such as ‘Good or Bad’, ‘True or False’, etc.
- Nominal or Unordered Data – Nominal or Unordered Data is the data which is in an unordered form such as ‘red’, ‘green’, ‘man’, etc.
- Ordinal Data – Ordinal Data is the data with proper order such as ‘short’, ‘medium’, ‘long’, etc.
2. Numerical or quantitative data
Numerical or quantitative data is the data that is based on numerical information such as ‘4 litres of water’, ’20 apples’, etc.
Numerical data can be further categorized into two other types of data in data science:
- Discrete Data – Discrete data is the data that is countable such as the number of children, whole numbers, etc.
- Continuous Data – Continuous Data is the data that is measurable such as height, width, length, etc. Continuous Data can be further broken down into two other types of data as given below,
- Interval – Intervals are data that can never be true zero such as the absence of temperature (0.000001 °C).
- Ratio – Ratio is the data that can be absolute zero such as height.
What do you think about this categorization? Let us know in the comments below.