Intro to data -datascience


Introduction to Data

Data is basically useful information about anything and of anybody. Data is commonly known as big data. this could be anything like the rating for a movie by a user, purchasing history, searching history, preferences of a user, likes and dislikes of a user, etc.

in the field of Data Science, the Data/Big Data is classified into two categories:-
  1. Qualitative Data
  2. Quantitative Data

    Qualitative Data

it deals with characteristics and descriptions that can't be measured easily but can be observed subjectively. Qualitative data is a type of data that describes the information. It is investigative and also often open-ended, allowing respondents to fully express themselves. it is also known as categorical data. this data type isn’t necessarily measured using numbers but rather categorized based on properties, attributes, labels, and other identifiers.
example:- qualifications,sex,name, etc.

this data is also classified into two new categories:-


  • Nominal data:- data which does not have any order and ranking  

    • example:-gender of population   

  • Ordinal data:- data which has an order or ranking 

    • example:- student information in the school register


      Quantitative Data

Quantitative data is any data that is in numerical form such as statistics, percentages, etc

it is defined as the value of data in the form of counts or numbers where each data-set has a unique numerical value associated with it. Quantitative data makes measuring various parameters controllable due to the ease of mathematical derivations they come with.

The quantitative data is also classified into two categories:-

  • Discrete Data:- data which hold finite number of entries typically countable

    • example:- No. of appointments in the office.

  • Continous Data:- data that can hold an infinite number of entries within a range or data with some error range.

    • example:- weight of a  person at the old weighing machine, this will vary every time

                             

Examples of quantitative and categorical data



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