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generate random number python

Introduction – Importance of Data

“Data is the new oil.” Today data is everywhere in every field. Whether you are a data scientist, marketer, businessman, data analyst, researcher, or you are in any other profession, you need to play or experiment with raw or structured data. Data has become an important asset for an organization.

Now business runs on data, most of the company’s uses data for their insights to create and launch campaigns, design strategies, launch products, and services or try out different things. According to a report, today, at least 2.5 quintillion bytes of data are produced per day.

The word “Data” arises from the Latin word “Datum,” which means “something given.” This data is so important for us that it becomes important to handle and store it properly, without any error. While working on these data, it is important to know the class of data to process them and get the right results. There are two classes of data: Qualitative and Quantitative data, which are further classified into four types: nominal, ordinal, discrete, and Continuous.

Also read : 22 Top Data Science Books – Learn Data Science Like an Expert

Types of Data

Qualitative or Categorical Data

Qualitative or Categorical 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, or text. The gender of a person, i.e., male, female, or others, is qualitative data.

Qualitative data tells about the perception of people. This data helps market researchers understand the customers’ tastes and then design their ideas and strategies accordingly. 

The other examples of qualitative data are :

  • What language do you speak
  • Favourite holiday destination
  • Opinion on something (agree, disagree, or neutral)
  • Colours

The Qualitative data are further classified into two parts :

Nominal Data

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

The name “nominal” comes from the Latin name “nomen,” which means “name.” 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 to distinct categories.

Examples of Nominal Data :

  • Colour of hair (Blonde, red, Brown, Black, etc.)
  • Marital status (Single, Widowed, Married)
  • Nationality (Indian, German, American)
  • Gender (Male, Female, Others)
  • Eye Color (Black, Brown, etc.)

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. 

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

Examples of Ordinal Data :

  • When companies ask for feedback, experience, or satisfaction on a scale of 1 to 10
  • Letter grades in the exam (A, B, C, D, etc.)
  • Ranking of peoples in a competition (First, Second, Third, etc.)
  • Economic Status (High, Medium, and Low)
  • Education Level (Higher, Secondary, Primary)

Difference between Nominal and Ordinal Data

  • Nominal data can’t be quantified, neither they have any intrinsic ordering
  • Ordinal data give some kind of sequential order by their position on the scale
  • Nominal data is qualitative data or categorical data
  • Ordinal data is said to be “in-between” of qualitative data and quantitative data
  • They don’t provide any quantitative value, neither we can perform any arithmetical operation
  • They provide sequence and can assign numbers to ordinal data but cannot perform the arithmetical operation
  • Nominal data cannot be used to compare with one another
  • Ordinal data can help to compare one item with another by ranking or ordering

Examples: Eye colour, housing style, gender, hair colour, religion, marital status, ethnicity, etc

Example: Economic status, customer satisfaction, education level, letter grades, etc 

Quantitative Data

Quantitative data can be expressed in numerical values, which makes it countable and includes 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, the computer’s ram, the height or weight of a person, etc., falls under the quantitative data. 

Quantitative data can be used for statistical manipulation and these data can be represented on a wide variety of graphs and charts such as bar graphs, histograms, scatter plots, boxplot, pie charts, line graphs, etc.

Examples of Quantitative Data : 

  • Height or weight of a person or object
  • Room Temperature
  • Scores and Marks (Ex: 59, 80, 60, etc.)
  • Time

The Quantitative data are further classified into two parts :

Discrete Data

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.

Examples of Discrete Data : 

  • Total numbers of students present in a class
  • Cost of a cell phone
  • Numbers of employees in a company
  • The total number of players who participated in a competition
  • Days in a week

Continuous Data

Continuous data are in the form of fractional numbers. It can be the version of an android phone, the height of a person, the 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 data such as temperature, height, width, time, speed, etc.

Examples of Continuous Data : 

  • Height of a person
  • Speed of a vehicle
  • “Time-taken” to finish the work 
  • Wi-Fi Frequency
  • Market share price

Difference between Discrete and Continuous Data

  • Discrete Data
  • Continuous Data
  • Discrete data are countable and finite; they are whole numbers or integers
  • Continuous data are measurable; they are in the form of fraction or decimal
  • Discrete data are represented mainly by bar graphs
  • Continuous data are represented in the form of a histogram
  • The values cannot be divided into subdivisions into smaller pieces
  • The values can be divided into subdivisions into smaller pieces.
  • Discrete data have spaces between the values.
  • Continuous data are in the form of a continuous sequence

Examples: Total students in a class, number of days in a week, size of shoe, etc

Example: Temperature of room, the weight of a person, length of an object, etc

In this article, we have discussed the types of data and the differences between them. Working on data is a crucial part because we need to figure out what kind of data it is and how to use it to get valuable output out of it. It is also important to know what kind of plot is suitable for which data category; it helps in data analysis and visualization. Working with data requires good data science skills and a deep understanding of different types of data and how to work with them. 

Different types of data are used in research, analysis, statistics, and data science. This data helps a company analyze its business, design its strategies, and help build a successful data-driven decision-making process. 

If these data driven topics got you interested in pursuing professional courses or career in the field of Data Science. Log on to our website and explore courses delivered by industry experts.

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