4.2 Data Types in Data Science

This topic explains the different types of data used in data science, including qualitative (categorical) and quantitative (numerical) data, and their subtypes.

Definition

Data Types in Data Science: In data science, we can mainly classify data into two main types: Qualitative (Categorical) and Quantitative (Numeric) .

Core Concept

Data types in data science are like different categories of ingredients in a kitchen. Just as you wouldn't mix up salt and sugar (even though they look similar), data scientists must distinguish between different types of data because each type requires different handling and analysis methods. Qualitative data describes qualities or categories (like colors or names), while quantitative data deals with numbers (like height or weight). Understanding data types is essential for choosing the right analysis technique.

Detailed Explanation

Qualitative or Categorical Data

Qualitative or Categorical data describes an object or a group of objects that can be labeled according to some group or category. It cannot be represented in numerical form. For example, data including colors, places, etc. It is further subdivided into two types:

• Ordinal Data
• Nominal Data

Ordinal Data: Ordinal data sees a specific order or ranking. It uses a certain scale or measure to group data into categories. Such as in test grades, economic status, or military rank.

• Has a specific order or ranking.
• Categories can be ranked.
• Examples: Test grades (A, B, C), economic status (low, middle, high), military rank.

Nominal Data: Nominal data does not have any order. It can be labeled into mutually exclusive categories, which cannot be ordered meaningfully. For example, if we consider the categories of transportation as car, bus, or train. Similarly, gender, city, color, employment status are also examples of nominal data.

• No order or ranking.
• Mutually exclusive categories.
• Examples: Transportation (car, bus, train), gender, city, color, employment status.

Quantitative or Numerical Data

Quantitative or Numerical data deals with numeric values that can be computed mathematically to draw some conclusions. Examples of numeric data are height, weight, number of students in a school, fruits in a basket, etc. Quantitative data can be further divided into two types:

• Discrete Data
• Continuous Data

Discrete Data: It includes data which can only take certain values and cannot be further subdivided into smaller units. This data can be counted and has a finite number of values. For example, the number of product reviews, tickets sold, computers in certain departments, employees in a company, etc.

• Can only take certain values.
• Cannot be subdivided into smaller units.
• Can be counted.
• Finite number of values.
• Examples: Number of reviews, tickets sold, computers, employees.

Continuous Data: It refers to the unspecified number of possible measurements between two realistic points or numbers. For example, daily wind speed, weight of newborn babies, freezer's temperature, etc.

• Can take any value within a range.
• Can be measured, not counted.
• Infinite number of possible values.
• Examples: Wind speed, weight, temperature.

Continuous data is further subdivided into two types:

• Interval Scaled Data
• Ratio Scaled Data

Interval Scaled Data: In this type of data, the differences between consecutive measurements are equal, but there is no true zero point. For example, temperature in Celsius or Fahrenheit (0°C or 0°F) does not mean the absence of temperature. Interval scaled data can be added, subtracted, and compared.

• Equal differences between measurements.
• No true zero point.
• Can be added, subtracted, compared.
• Examples: Temperature in Celsius or Fahrenheit.

Ratio Scaled Data: In this type of data, the differences between the consecutive measurements are meaningful. There is a true zero point in this sort of data. For example, 0 Kg means absence of weight. This data can be added, subtracted, and compared.

• Meaningful differences between measurements.
• True zero point.
• Can be added, subtracted, compared.
• Examples: Weight, height, age.

Table (Relation)

A table is a combination of rows and columns, which can store data about a particular person, place, thing, or event.

Rows (Record, Tuple, Object): A row is a single entry or record in the table. It is also called an instance of the table.

Column (Object Attributes, Properties): A column is a single characteristic or field in the table.

Example: A student table can contain attributes name, age, grade. One row will contain data about one particular student.