Data and types of Data- Nominal, Ordinal, Discrete and Continuous
Table of Contents
Data
Requirement of data
Files
Types of Data
Categorical Data
Numerical Data
Data
Data is a set of values or facts that can be collected , stored, and analyzed . Data can be come in forms of number, text, images or audio recording.
Requirement of data (Properties):
- Integrity (accuracy and consistency. Ex: Name).
- Availability (like available for 24*7).
- Security (Data should be secure).
- Independent of application (Same data available across every platform).
- Concurrency (Got same data whose has access).
Why we not use Files ?
- Dependency of program on physical structure on data.
- Complex process to retrieve data.
- Loss of data on concurrent access.
- Inability to give access based on record (security).
- Data redundancy (repetition of the same data).
When to use Files ?
- On device access (Offline access. Ex: Chat).
- Storing frequent incoming data.
- Storing not so important data.
Types of Data
- Categorical(Qualitative)
- Nominal Data
- Ordinal Data
2. Numerical (Quantitative)
- Discrete Data
- Continuous Data
1. Categorical (Qualitative)
Qualitative or Categorical Data is data that can’t be measured or counted in the form of numbers. These types of data are sorted by category, not by number. That’s why it is also known as Categorical Data. These data consist of audio, images, symbols, or text. The gender of a person, i.e., male, female, or others, is qualitative data.
Qualitative data tells about the perception of people. This data helps market researchers understand the customers’ tastes and then design their ideas and strategies accordingly.
Examples of qualitative data
- Gender (male, female, other)
- Marital status (single, married, divorced, widowed)
- Education level (high school, college, graduate school)
- Ethnicity (Caucasian, African American, Hispanic, Asian, etc.)
- Occupation (teacher, doctor, lawyer, etc.)
Qualitative data are classified into two parts :
i. Nominal Data
Nominal Data is used to label variables without any order or quantitative value. The color of hair can be considered nominal data, as one color can’t be compared with another color.
The name “nominal” comes from the Latin name “nomen,” which means “name.” With the help of nominal data, we can’t do any numerical tasks or can’t give any order to sort the data. These data don’t have any meaningful order; their values are distributed into distinct categories.
Example of Nominal Data
- Gender: male, female, other
- Marital status: married, single, divorced, widowed
- Eye color: blue, brown, green, gray, hazel
- Hair color: black, brown, blonde, red, gray
- Ethnicity: African American, Asian, Caucasian, Native American, Other
ii. Ordinal Data
Ordinal data have natural ordering where a number is present in some kind of order by their position on the scale. These data are used for observation like customer satisfaction, happiness, etc., but we can’t do any arithmetical tasks on them.
Ordinal data is qualitative data for which their values have some kind of relative position. These kinds of data can be considered “in-between” qualitative and quantitative data. The ordinal data only shows the sequences and cannot use for statistical analysis. Compared to nominal data, ordinal data have some kind of order that is not present in nominal data.
Example of Ordinal Data
- Educational level (e.g. high school, bachelor’s , master’s , PhD)
- Levels of customer satisfaction (e.g. very satisfied, satisfied, neutral, dissatisfied, very dissatisfied)
- Ratings on a Likert scale (e.g. strongly agree, agree, neutral, disagree, strongly disagree)
- Letter grades in a class (e.g. A, B, C, D, F)
- Rankings of sports teams in a league (e.g. 1st place, 2nd place, 3rd place)
Difference between Nominal and Ordinal Data
2. Numerical (Quantitative)
Quantitative data is a type of data that is expressed in numerical values, making it countable and suitable for statistical data analysis. It answers questions like “how much,” “how many,” and “how often.” Examples of quantitative data include the price of a phone, the computer’s RAM, and the height or weight of a person.
Quantitative data can be used for statistical manipulation and represented using a variety of graphs and charts, such as bar graphs, histograms, scatter plots, box plots, pie charts, and line graphs.
Types of Quantative Data
Discrete Data
The term discrete means distinct or separate. The discrete data contain the values that fall under integers or whole numbers. The total number of students in a class is an example of discrete data. These data can’t be broken into decimal or fraction values.
The discrete data are countable and have finite values; their subdivision is not possible. These data are represented mainly by a bar graph, number line, or frequency table
Examples of Discrete Data :
- Number of siblings in a family
- Number of pets owned by a person
- Number of cars in a parking lot
- Number of rooms in a house
- Number of items sold in a store
Continuous Data
Continuous data are in the form of fractional numbers. It can be the version of an android phone, the height of a person, the length of an object, etc. Continuous data represents information that can be divided into smaller levels. The continuous variable can take any value within a range.
The key difference between discrete and continuous data is that discrete data contains the integer or whole number. Still, continuous data stores the fractional numbers to record different types of data such as temperature, height, width, time, speed, etc.
Examples of Continuous Data :
- Temperature readings
- Height of a person or object
- Weight of a person or object
- Time taken to complete a task
- Length or width of an object
Difference between Discrete and Continuous Data
Conclusion
In this article, we have discussed the data ,types of data and their differences. Working on data is crucial because we need to figure out what kind of data it is and how to use it to get valuable output out of it. It is also important to know what kind of plot is suitable for which data category; it helps in data analysis and visualization. Working with data requires good data science skills and a deep understanding of different types of data and how to work with them.
Different types of data are used in research, analysis, statistical analysis, data visualization, and data science. This data helps a company analyze its business, design its strategies, and help build a successful data-driven decision-making process.
