Types of data we can deal with
Tags: datascience, statistics
Published: 2021-02-24 18:48:01

There are different types of variables which we encounter when dealing with business analytics. The type of data we are going to work with are divided into two main ways.

__1. Type of data__ 
__2. Measurement levels__

__1. Type of data__

It is further divided into __numerical__ and __categorical__ data. 

__Categorical data__

It defines categories or groups. Car brands like Tata, Honda, Toyota etc.. are considered to be different categories. Another example is the Yes/No questions. When someone asks us whether we are above 18 years of age or not, we have only two answer categories (Yes and No).

__Numerical data__

As the name suggests, this data represents numbers. It is further divided into two types , __discrete__ and __continuous__.

* __Discrete numerical data__ :

 Discrete data can be counted in a __finite__ manner. For example, you can count the change in you pocket, you can also count the money in your bank account. Even if we want to count the money in everyone's bank account, it is feasible and only thing is it would take a considerable amount of time. It is actually the opposite of continuous data.

* __Continuous numerical data__ :

Continuous numeric data is __infinite__ and impossible to count. It is never possible to count them in a particular time interval. For example age, we cannot count age. Why? Because it would take more time. You would be __11 years, 11 months, 11 days, 11 hours, 11 minutes, 11 seconds etc.... __, and the __time factor__ is continuous and it keeps on changing. So at any fixed point of time, we cannot calculate our age.

__How to calculate age then ?__

The above age value is a continuous value and it keeps on changing, the only way to calculate the age of a person is to convert the age value to a discrete variable and then count it. We remove the time factor (continuous value) from the age.
Time on a clock is discrete, but in general it is continuous. Now it becomes a discrete variable and we can calcul ate the age. The age value will be 11 years or 300 months. 

__Other examples__ of continuous data include the height, weight, age , time, area etc..

__2. Measurement levels__

Levels of measurement are split into two groups, __qualitative__ and __quantitative__ data. 

__QUALITATIVE DATA__

Qualitative data can be categorised based on traits and characteristics. For example: cars can be categorised based on their brand name and a food can be categorised based on their reviews. Qualitative data can be __nominal__ or __ordinal__.

* __Nominal data__ 

Nominal variable are like the car categories, __they aren't numbers__ and __cannot be ordered__ . Here the options can be chosen individually but they don't have any order here. So the measurement level here is qualitative and nominal.

* __Ordinal data__

Ordinal data consists of groups and categories that __follow a strict order__. For example when we are asked to rate the food in a restaurant, the options to rate are stale, good ,very tasty, must try etc... Although these options does not represent numbers, these options are __ordered from negative to positive__ values in terms of rating. Thus the measurement level here is qualitative and ordinal.

__QUANTITATIVE DATA__

They are data represented by numbers. Usually there are also measurement units associated with data , eg. 12 metres, 10 kg etc.. It is data expressing a certain quantity, range or amount. They are further split into __interval__ and __ratio__.

* __Interval data__

Interval allows you to measure all quantitative attributes. Any measurement of interval can be ranked, added, subtracted etc.. and equal intervals separate each quantitative value. It will also have its lower bound less than zero. For example: a temperature value is a quantitative data and it can have both negative and positive values, so this is said to be interval data.

* __Ratio__

It also has the same properties as interval data. It can also be used to add, subtract, count etc.. Ratio values never fall below zero. Height and Weight never fall below zero, and they always measure from zero.

__Interval ratio scale measurements__

Understanding the different scales of measurements allows you to identify the __different types of data__ you can gather. These differences will help us to identify the kind of statistical research required for your research.

__Interval level of measurement__

It specifies the distance between each interval of a scale is equivalent, from low interval to high interval. For example , the difference between 10 degree C and 20 degree C is the same as the difference between 20 degree C and 30 degree C.

__Ratio level of measurement__

Ratio levels __can have a value of zero__. It states that the cost difference between two cars that are 100000 dollars and 150000 dollars is the same as the cost difference between two cars that are priced 200000 dollars and 250000 dollars respectively. However, you won't be able to find cars priced less than zero.