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# What is DATA? Data is a collection of discrete values that convey information, describing quantity, quality, fact, other basic units of meaning or simply sequences of symbols that may be further interpreted. In simple terms, data is defined as facts, information or value(figures) etc. Data can come in any forms i.e. figures, text, information, numbers, graphs and observations etc. So, we can understand that data is raw form of knowledge.

In our day-to-day activities, we use or talk about data. In this blog we will discuss different aspects of data.

## 1. Types of data?

There are two types of data: Qualitative and Quantitative data, which are further classified into four parts.

• Nominal data.

• Ordinal data.

• Discrete data.

• Continuous data. A. Qualitative or Categorical Data

Qualitative Data is data that can’t be measured or counted in the form of numbers. These types of data are sorted by category, not by number. That’s why it is also known as Categorical Data. These data consist of audio, images, symbols, text, gender etc.

The Qualitative data are further classified into two parts :

i). Nominal Data

Nominal Data is used to label variables without any order or quantitative value. The color of hair can be considered nominal data, as one color can’t be compared with another color.

With the help of nominal data, we can’t do any numerical tasks or can’t give any order to sort the data. These data don’t have any meaningful order, their values are distributed into distinct categories.

ii). Ordinal Data

Ordinal data have natural ordering where a number is present in some kind of order by their position on the scale. These data are used for observation like customer satisfaction, happiness, etc., but we can’t do any arithmetical tasks on them.

Ordinal data is qualitative data for which their values have some kind of relative position. These kinds of data can be considered “in-between” qualitative and quantitative data. The ordinal data only shows the sequences and cannot use for statistical analysis. Compared to nominal data, ordinal data have some kind of order that is not present in nominal data.

B. Quantitative Data

Quantitative data can be expressed in numerical values, making it countable and including statistical data analysis. These kinds of data are also known as Numerical data. It answers the questions like “how much,” “how many,” and “how often.” For example, the price of a phone, laptop memory, the height or weight of a person, etc., falls under quantitative data.

Quantitative data can be represented on a wide variety of graphs and charts, such as bar graphs, histograms, scatter plots, box plots, pie charts, line graphs, etc.

The Quantitative data are further classified into two parts :

i). Discrete

The term discrete means distinct or separate. The discrete data contain the values that fall under integers or whole numbers. The total number of students in a class is an example of discrete data. These data can’t be broken into decimal or fraction values.

The discrete data are countable and have finite values, their subdivision is not possible. These data are represented mainly by a bar graph, number line or frequency table.

ii). Continuous

Continuous data are in the form of fractional numbers. It can be OS version, height of a person, length of an object etc. Continuous data represents information that can be divided into smaller levels. The continuous variable can take any value within a range.

The key difference between discrete and continuous data is that discrete data contains the integer or whole number. Still, continuous data stores the fractional numbers to record different types of data such as temperature, height, width, time, speed, etc.

## 2. From where does data come and how it can be collected?

Data exists all around us. It can be collected in many forms. For eg:

• Questionnaires.

• Documents.

• Records and surveys.

• Interviews, observations, histories and many more.

## 3. What are the benefits of data?

Data can be helpful in many ways. Some written below:

• Optimize the quality of work.

• Predict trends.

• Prevent risks.

• Save time.

• Drive profits and make better decisions etc.