Data/basics of data

What do I need to know about Data — Data 101

Data basics — This post covers the aspects such as, what is data, why do we need data, and how is the data collected.

Data is a collection of facts and dimensions.

  • Qualitative — Non numerical data
  • Quantitative — numerical data

It is the non-numerical data that is used to classify and categorise things/elements. For instance, let’s talk about coffee !!

Coffee has a strong aroma, tastes good, topped with cream and chocolate powder, the cup is made of ceramic and is round in shape. Coffee also contains milk and sugar.

Let’s consider, you have a box of T-shirts and you can classify data of T-shirts based on the size — Small, medium, large, and based on colour. All blues, all whites.

It is the numerical data that can be measured.

For instance the latte contains 12 ounces of milk, 2 spoons sugar, It costs 4 euro. It is served in a cup with dimensions — 12 X 15 X 16

Two types of quantitative data

  • A discrete data which involves counting whole numbers. For instance, a family that has 2 children and 1 pet. This family cannot have 1.5 child and 2.5 dogs.
  • Continuous data — any numerical data of a certain range. For instance, a person’s height or temperature.
  • Primary sources — Data collected by researchers by conducting interviews, surveys, case studies, observations, questionnaires.
  • Secondary sources — web info, govt info, previous research papers.

Data can be used to tell a story, find patterns, analyse and predict, provide insights and help make impactful decisions in all industries.

For example, students that spend a lot of time on social media have poor grades.

To Tell a story on climate change using data — you need data about global temperature patterns, cycles etc.

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Lead Technical writer for Data Platform & loves to share my learnings about data, productivity, tech writing, self-help & more|A Data Analytics aspirant

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Navya G

Lead Technical writer for Data Platform & loves to share my learnings about data, productivity, tech writing, self-help & more|A Data Analytics aspirant