---
title: Types of Data
description: "There are several types of data. They are often generalized into two groups: **continuous** and **categorical**. ## **Continuous** Continuous data is able to assume any real numerical value and can be defined on a continuous scale. It comprises the numeric…"
url: "https://sdscope.com/doc/types-of-data/"
updated: "2022-06-02"
category: 5. Data Understanding
---

# Types of Data

There are several types of data. They are often generalized into two groups: **continuous** and **categorical**.

## **Continuous**

Continuous data is able to assume any real numerical value and can be defined on a continuous scale. It comprises the numeric and interval types.

- **Numeric:** whole number or fraction, positive or negative number that allows full arithmetic operations (eg, weight, temperature, price)
- **Interval:** number that allows ordering, addition and subtraction operations only (eg, salary scale, date, time)

## **Categorical**

Categorical data takes a finite set of values and does not allow arithmetic operations. The common types of data in this category are listed below.

- **Nominal:** only takes a limited set of values with no meaningful intrinsic ordering (eg, color, country, profession)
- **Ordinal:** only takes a limited set of values with a meaningful intrinsic ordering (e.g., size coded as small, medium, or large; product rating; fault severity coded as minor, major, or critical)
- **Binary:** only takes two values (eg, gender; color coded as bright or dull; time of day coded as day or night)
- **Text:** free-form text data (e.g., name, address, emails, documents, reviews, social media posts)
- **Image data:** stored as pixels containing information about color and intensity
- **Geospatial data** contains location information
- **Network data** contains data about how items are connected and the strengths and directions of the relationships

## Why Understanding Data Types is Important?

Assigning the correct type to a feature is critical because it determines how the data will be stored and how it will be used by machine learning algorithms. Consider a variable "profession" coded as 1, 2, 3, etc, where 1 = nurse, 2 = engineer, 3 = plumber, etc. If it were to be incorrectly specified as a numeric variable, then machine learning algorithms would calculate its sum, average, standard deviation, and so on, which is obviously meaningless.

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