The document explains different types of data in data science, categorizing them into qualitative and quantitative data, with subcategories for each. Qualitative data includes nominal and ordinal types, while quantitative data is divided into discrete and continuous types. Additionally, data is classified according to format into structured, semi-structured, and unstructured data.

1. Data Science
Different types of data

2. Types of
Data
Qualitative
Nominal Ordinal
Quantitative
Discrete Continuous

3. 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

4. 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).

5. 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.

6. 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

7. 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.

8. 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.

9. Types of Data
according to
format
Structured Semi-Structured Unstructured

10. 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.

11. 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.
1. 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.
2. 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.

12. 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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