Introduction: Why is data Important?
Data has become a day-to-day life need. Everything you see is data, you are reading this blog post is data too. We use data whether you are a data scientist or a Business owner or data analyst or you are in any other profession.
You need to use and experiment with the data. The world uses data, every day more than 2.5 quintillion bytes of data are produced and it is very important to handle and store it properly without any errors.
In order to manage the data, the type of data plays an important role. The category of data helps to determine which strategy would work to get the right results.
There are 2 Types of Data:
-
Qualitative Data Type
-
Quantitative Data Type
Let's look at each of these and What are Qualitative and Quantitative Data.
1) Qualitative Data Type
Qualitative data is also called Categorial data. It is data that can't be measured or counted in numbers. That's why it is divided into categories and is called Categorial data. This type of data consists of audio, images, symbols, or text.
Take the gender of a person as an example, we can't count it in numbers, but we can categorize it in its categories male, female, or others. It is qualitative data.
This type of data helps researchers to understand the desires of the customers and then they can design or strategize campaigns accordingly.
There are 2 subtypes of Qualitative data:
- Nominal data
- Ordinal data
Nominal data
There is a type of data that can't be sorted in order. Let's say if you have 2 T-shirts, one has a Blue color and the other has a Yellow color. Now, you can't compare the colors to each other like Blue is greater than yellow.
We can't do any numerical tasks with Nominal data as it doesn't have any order. The name "Nominal" comes from a Latin word called "nomen" which means "name". The data is distributed into categories and nominal data can't be counted into numbers.
Nominal Data Example:
- Marital status (Single, Widowed, Married)
- Nationality (Indian, German, American)
- Gender (Male, Female, Others)
Ordinal Data
Unlike Nominal data, Ordinal data has order because a number is present in order by its position. Considering you have to buy clothes online, you can easily sort them according to their name such as small, medium, and large. You know the "large" size is bigger than other sizes.
Similarly, In the grading system if you got an A+ and your friend got a B. We know A+ is greater than a B grade. This data is also considered Ordinal data.
Ordinal data is considered as "in-between" qualitative and quantitative data. It has some kind of order than Nominal data doesn't.
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Example of Ordinal data:
- Letter grades in the exam (A, B, C, D, etc.)
- Ranking people in a competition (First, Second, Third, etc.)
- Education Level (Higher, Secondary, Primary)
Difference Between Nominal and Ordinal Data
here is the difference between Nominal and Ordinal Data -
| Nominal Data |
Ordinal Data |
| Represents categories or names |
Represents categories or names that can be ranked or ordered |
| Categories are not ordered or ranked in any particular way |
Categories are ranked or ordered based on some criteria, such as frequency or importance |
| No inherent order or sequence |
Has a defined order or sequence |
| Examples: gender, eye color, race, religion |
Examples: Likert scales (e.g. "strongly agree" to "strongly disagree"), levels of education (e.g. "high school" to "doctorate"), socioeconomic status (e.g. "low-income" to "high-income") |
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2) Quantitative Data Type
Quantitative data have numerical values that's why it's countable and suitable for statistical data analysis. This data answers questions like "how much" and "how many".
The price of a phone, the ram of that mobile, number of ratings of a product are examples of Quantitative data.
This type of data can be used in statistical manipulation and can be represented as bar graphs, histograms, line graphs, etc.
There are 2 subtypes of Quantitative data:
- Discrete data
- Continuous data
Discrete Data
Discrete data has integers or whole numbers such as the number of speakers in a mobile, the number of cores in the processor, etc.
This data can be represented as a decimal number but must be whole. It cannot be measured in statistics as it has a fixed value. Discrete data can be represented by bar graphs, number lines, pie charts, and tally charts.
Examples of Discrete Data:
- Price of a cell phone
- Numbers of employees in a company
- Days in a month
Continuous data
Continuous data have fractional values. The version of Android, wifi frequency, length of an object, etc are examples of continuous data.
Unline discrete data type that holds integers or whole values, and continuous data type have fractional numbers. The temperature or height of a person falls under continuous data. it can be represented as a graph that easily reflects value fluctuation.
Examples of continuous data:
- Height of a person
- Speed of a vehicle
- Wi-Fi Frequency
- Market share price
Conclusion
Understanding the different types of data is crucial for effectively managing and analyzing data. Qualitative data, which includes categorical data, is important for understanding customer desires and designing effective strategies. Nominal data, which cannot be sorted in order, and ordinal data, which has a specific order, are subtypes of qualitative data.
On the other hand, quantitative data, which includes numerical values, is suitable for statistical data analysis and can be represented using various graphs. Discrete data, which has whole numbers, and continuous data, which has fractional values, are subtypes of quantitative data. Knowing the type of data is essential for determining the appropriate analytical approach and drawing accurate conclusions.
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Related Questions
1). What are 3 examples of Discrete Data?
3 Examples of discrete data could be, The number of people in a class, Test questions answered correctly, and Home runs hit in a game.
2). What is the difference between Nominal and Ordinal data?
The difference between Nominal and Ordinal data is that Nominal data categorizes variables into non-numerical categories, while Ordinal data categorizes variables into ordered categories or ranks. Nominal data does not have an inherent order, while ordinal data does.
3). What are examples of Nominal data?
Examples of nominal data can be, Marital status (Single, Widowed, Married), Nationality (Indian, German, American), Gender (Male, Female, Others)
4). Can there be combined data types?
Yes, there can be combined data types that are considered more complex. For example, temperature data can be both continuous and ordinal if it is categorized into specific temperature ranges, such as cold, warm, and hot.
