Contents

**Data|Definition & Meaning**

**Definition**

**Data** is a collection of **discrete** values describing information, quantity, quality, fact, statistics, other basic units of the essence, or just a series of entities that might be **further analyzed**.

**Data** can be any group of **characters** that is compiled and translated for some **meaning**, usually **research**. **D****ata** doesn’t do anything to a human or any computer if it is not translated to have a **meaning**.

**What Is Data in Math?Â **

**Data** in math is a group of **facts and figures** that can be in either **numerical** form or **non-numerical **form**.** **Numerical data** is the one you can **compute. Numerical data** is composed of **numeric forms**, such as the marks of students in **class,** the wages of players on a football team, and the height of workers in an organization.

Data that can only be** collected** and not computed is called **Non-numerical data. **Examples of Non-numerical data are the personality of a person or the animal’s physical appearance. **Non-numericalÂ **data can be any observation that does** not include numbers**. The taste of some food or the color of a pencil are also some examples of **numeric** data.

**Types of Data in Math**

Figure 1 categorizes the types of data we usually encounter in math. Below, we dive deeper into their descriptions.

**Qualitative Data**

**Qualitative data**, also known **as categorical data**, defines the data that checks into types. **Qualitative data** are not **numerical**. **Categorical** information describes the features such as a personâ€™s hometown, or sex, etc. **Categorical** data is described in terms of **language**, but not in terms of numbers.

Although sometimes **categorical**Â **data** can have **numerical** values (**quantitative** value), those values do not make much sense in a **mathematical way**. Examples of categorical data are date of birth, favorite sport, and school ID. Here, the date of birth and school ID holds the **quantitative** value, but it does not give a numerical sense.

**Nominal Data**

**Nominal data** comes under the types of **qualitative** data which helps to mark the variables without** feeding the numerical value**. **The nominal scale** is also a name for** nominal data**. It cannot be called and gauged. Some examples of **nominal** data are applications, words, emails, gender, etc.

The **nominal data** are analyzed by operating the **grouping technique**. In this technique, the data is divided into **classifications **by forming groups, and then the percentage of the data can be** estimated.** To visually represent the data, we use **pie charts.**

**Ordinal Data**

**Ordinal data** is a sort of data that obeys a natural order. The difference between the values is not determined which is an important feature of the nominal data. This variable is mostly seen in surveys,Â questionnaires, economics, and so on.

The **ordinal** data is generally represented using a bar chart. These data are looked into and analyzed through many visualization tools. The knowledge may be represented using tables in which each row in the table shows a different category.

**Quantitative Data**

**Quantitative data** is also understood as **numerical** data which illustrates the numerical value (i.e., how much, how many). **Numerical** data provides details about the **quantities** of a distinctive thing. Numerical data can be the length, size, height, distance, weight, and so on. **Quantitative** data can be categorized into two separate types based on the data sets. The **numerical** data is divided in two further categories that are **discrete** data and **continuous** data.

**Discrete Data**

**Data **comprised of discrete values is called **Discrete data**. Discrete information comprises only a** limited number of conceivable values.** Those values cannot be subdivided without them being meaningless. Things can be estimated in whole numbers here. The number of people attended the gym in January is an example of **Discrete data**.

**Continuous Data**

**Continuous data** is data that can be computed. It has an **infinite number of potential values** that can be set within a given distinct range.

Example: Range of temperature.

**Further Classification Into Primary and Secondary Data**

**Primary data** is authentic knowledge that has been gathered for a specific purpose. It is the **pure and natural form** of information that has never been worked upon. It is systematized in a structured way so it will be **useful in the future.**Â **Primary data** can be **qualitative** or **quantitative** but it can never be emanated from another reference. Census is an instance of **primary** data.

**Data** that is derived from a current data source, either in an unpublished or published document is called **Secondary **Data. It can be any data which is already been gathered and is reachable to any researcher for their use. **Secondary **Data can also be a **primary** data source. Information in government statistics, or journals, etc which are extracted from some primary information are examples of such data.

**Data in Computers**

In computing, data is the **knowledge** that has been translated into a form that is **meaningful for movement or processing**. Proximate to today’s computers and transmission media, data is information altered into **binary digital form**. It is permissible for data to be utilized as a **plural subject** or a **singular subject**. **Raw data** is a phrase utilized to explain data in its most authentic or basic digital format.

**Data Processing Cycle**

**Data** processing is defined as the **structuring** of data again by people or devices to increase its usability and add value to a specific **role or meaning.** Generally, data processing consists of three basic stages which are **input, processing, and** output** respectively**. Together, the data processing cycle is made up of these three steps.

**Input:**The data being input gets**formulated**for processing in a suitable form that depends on the machine holding the main processing.**Processing:**Next, the input dataâ€™s form is**transformed**into something more**meaningful or useful**for the processor. For example, information from the clock is used to calculate time.**Output:**In the last stage, the results of processing are compiled as output data, with its final state relying on the requirement of the user.

## Example of Utilizing Data for Meaningful Information

The height of students from one class is mentioned below:

Who is the **shortest** and **tallest** boy in the class?

**Solution**

1 foot is equal to 12 inches.

After arranging the data in ascending order:

**4′**, 4’3″, 4’9″, 4’10”, 5’1″, 5’4″, 5’5″ **5’9″**

The height of the tallest student is **5 feet 9 inches**, and the height of the shortest student is **4 feet**.

*All images/mathematical drawings were created with GeoGebra*.