Types of Data

In today’s world where tons of data is being generated and consumed in a second . It is a bit difficult to categorize it . However some of the categories under which it can be classified are as follows . Have a look at the image given below you will have a better understanding

Data is mainly classified into two categories :

• Qualitative Data
• Quantitative Data

1. Quantitative Data

This type of data answer questions like how much , how many , no of times . It is basically the representation of data through numerical figures. For a better understanding , have a look at the following examples.

1. The Burj Khalifa is the tallest building in the world having a total height of 2717 ft.
2. Mukesh drives his car at a speed of 90km/hr
3. Today’s temperature in shimla is recorded very low , (3°C) by the weather department
4. Raghav has scored highest marks (97/100) in his unit test of chemistry
5. Sunil was declared overweight by the doctor as his weight turned out to be 95 kg

These are all real life examples . Here height , speed , temperature , marks and weight represents the numerical value of the quantity given

2. Qualitative Data

This type of data cannot be measured through numbers . Or we can also say that , any data which comes out of the bracket of quantitative data is termed as qualitative data . Here are some examples

1. Aman was feeling sad today
2. Riya hair is brown in colour
3. Children went to zoo and captured photos with animal
4. Researchers documented the results of experiment in the manual
5. They went to watch the movie ‘Section 375’ . The genre of the movie was thriller .

Have a closer look at the above examples , you will not find any data which shows the value numerically , this is what qualitative data , it is a collection of images , videos , case studies , emotions , genre , etc . Basically anything which can;t be represented in number

Now the Quantitative Data and Qualitative Data , is also further divided into sub types . They are as follows

Quantitative Data

1. Discrete Data
2. Continuous Data

Qualitattive Data

1. Nominal Data
2. Ordinal Data

1) Discerte Data

This type of data represents the information that can be counted and measured in a limited number of separate values. The values are usually whole numbers and comes through counting/categorizing them. This type of data is actually a bit different from continuos data , which holds unlimited value swithin a specified range Example

• Number of students present in a class : You can count the exact number of students in a class . They are counted in whole number .
• Number of bikes present in a parking : You can get the exact value of no of bikes present in the parking . And again , it is a whole number
• Number of cards present in a deck : The total no of cards present in a deck is 52, and the total value is in whole number
• Number of emails received in a day : If you are student/working professional , you will be receiving emails daily . And that can also be counted , as they are present in a whole number .

Example : Look at the graph below , it shows the no of cars parked in a office , on different time . In the morning 8: 00 am , as the office time does not start , the count of cars is 0 , as the day progresses around 10 : 00 am , the count of car gets increased to 5, (office time starts) . Around 2 : 00 pm the no of cars is maximum , as it includes existing no of people along with the one whose shifts starts after 12 pm , if you look at the time after 4 : 00 pm no of cars decrease as mostly people start leaving the office .

2) Continuous Data

This type of data , holds the information that can have any value within a specific range . Unlike the discrete data which has separate and distinct values , continuous data can be divided into smaller and more precise values Example

• Height of a person : A person height can vary within a specific range and can be accurately measured.
• Weight of an object : The weight of an object , can change continuously within a certain range and it can be measured
• Temperature : Temperature can have any value within a specific range, and it can be measured with different levels of accuracy.
• Time : Time is commonly seen as continuous because it can be divided into increasingly smaller units without any noticeable breaks between them.
  Example : Look at the graph below , it shows the temperature of top 5 metro cities ( Mumbai , Delhi , Ahmedabad , Bengaluru ) upto 10 hours . You will find that temperature lies between the range of 20°C. to 30°C . As the line is pretty much straight . There has not been much drop or increase in the temperature.

1.) Nominal Data

Nominal data is a form of categorical data that represents categories or labels without any inherent order or ranking. In nominal data, the categories are separate and do not have a specific order or numerical value assigned to them. This kind of data is qualitative in nature , and the categories are used to classify items or observations into groups . Example

• Colors: Categories such as red, blue, yellow, etc., are considered nominal since they do not have any inherent order
• Gender: Male and female are nominal categories that represent separate groups, but there is no inherent ranking or order between them.
• Types of fruits: Categories like watermelon, orange, banana, etc., are nominal as they represent various types of fruits without any specific order.
• Marital Status : Marital status such as married, single, divorced, and widowed are considered nominal categories because they do not have any specific order.
• Types of Car : Car brands like Hyundai, Suzuki, Honda, and others are considered nominal categories as they group cars based on their brand without any specific ranking.

Example : Consider this graph , it categorizes no of electronic devices owned by different people in a particular company . You will find that maximum people are owing smartphone , where as smart tv holds the minimum count.

2.) Ordinal Data

Ordinal data is a special kind of categorical data that shows categories with a natural order or ranking. Unlike nominal data, where categories have no specific order, ordinal data allows for a meaningful comparison of values based on their relative position or rank. Examples

• Education Levels : High School Program , Associate Program , Bachelors Program , Masters Program ,
• Movie Ratings : 1 star , 2 star , 3 star , 4 star , 5 star
• Exercise Frequency : Rarely , Occasionally , Regulary , Frequently
• Temperature Levels : Very Cold , Cold , Moderate , Warm , Hot
• Educational Grades : A , B , C , D , F

Example :

Consider this graph given below , it shows the data of no of people who have visited the shopping store . Upon feedback received from the customers , As the ordinal data is a type of categorical data , satisfaction level ranges from very dissatisfied to very satisfied

Data science primarily revolves around extracting meaningful insights from vast datasets, Data-science storytelling takes the world of data analysis and adds the storytelling touch to it.

In this article, we will learn How Data Storytelling works in data science, How it helps to visualize data, How to make a good data story, and Future of the Data Storytelling.