Different types of data. Qualitative, Quantitative, Ordinal, Nominal, Discrete, Continuous.

Different types of data. Qualitative, Quantitative, Ordinal, Nominal, Discrete, Continuous.

1. Data Science Different types of data

2. Qualitative Data Qualitative data is information that represents some characteristics or attributes. It depicts descriptions that cannot be counted, measured, or easily expressed with the help of numbers. It can be collected from audio, text, and pictures. It is shared via data visualization tools, such as concept maps, clouds, infographics, timelines, and databases. For instance, collecting data on attributes such as honesty, intelligence, creativity, wisdom, and cleanliness about students of any class would be considered as a sample of qualitative data. Qualitative data is further categorized into two categories that includes, • Nominal Data • Ordinal Data

3. Nominal Data Nominal data is a type of data that consists of categories or names that cannot be ordered or ranked. Nominal data is often used to categorize observations into groups, and the groups are not comparable. In other words, nominal data has no inherent order or ranking. Examples of nominal data include gender (Male or female), race (White, Black, Asian), religion (Hinduism, Christianity, Islam, Judaism), and blood type (A, B, AB, O).

4. Ordinal Data Ordinal data is a type of data that consists of categories that can be ordered or ranked. However, the distance between categories is not necessarily equal. Ordinal data is often used to measure subjective attributes or opinions, where there is a natural order to the responses. Examples of ordinal data include education level (Elementary, Middle, High School, College), job position (Manager, Supervisor, Employee), etc.

5. Quantitative Data These types of data can be measured but not simply observed. The data can be numerically represented and used for statistical analysis and mathematical calculations. For example, these mathematical derivations can be used in real-life decisions. Also, the number of students participate in different games from a class; the mathematical calculation gives an estimate of how many students are playing in which sport. Quantitative data is further classified into two categories that are: • Discrete Data • Continuous Data

6. Discrete Data Discrete data type is a type of data in statistics that only uses Discrete Value or Single Values. These data types have values that can be easily counted as whole numbers. The example of the discrete data types is: • Height of Students in a class • Marks of the students in a class test • Weight of different members of a family, etc.

7. Continuous Data Continuous data is the type of quantitative data that represent the data in a continuous range. The variable in the data set can have any value between the range of the data set. Examples of the continuous data types are: • Temperature Range • Salary range of Workers in a Factory, etc.

8. Structured Data Structured data refers to data that is organized and formatted in a specific way to make it easily readable and understandable by both humans and machines. This is typically achieved by a well-defined schema or data model, which provides a structure for the data. Structured data is typically found in databases and spreadsheets and is characterized by its organized nature. Each data element is typically assigned a specific field or column in the schema, and each record or row represents a specific instance of that data. For example, in a customer database, each record might contain fields for the customer’s name, address, phone number, and email address.

9. Semi structured Data Semi-structured data is a type of data that is not purely structured, but also not completely unstructured. It contains some level of organization or structure but does not conform to a rigid schema or data model, and may contain elements that are not easily categorized or classified. Semi-structured data is typically characterized by the use of metadata or tags that provide additional information about the data elements. For example, an XML document might contain tags that indicate the structure of the document but may also contain additional tags that provide metadata about the content, such as author, date, or keywords. Other examples of semi-structured data include JSON, which is commonly used for exchanging data between web applications, and log files, which often contain a mix of structured and unstructured data.

10. Unstructured data Unstructured data  is the data which does not conforms to a data model and has no easily identifiable structure such that it can not be used by a computer program easily . Example: Text documents, image data

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