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What is Data Analysis?

So far we have studied what is data, but to come to a conclusion, or to extract meaningful information from Data, we need to analyze/look into our Data first. This analysis process is called Data Analysis.

Why do we do it?

To get meaningful information

What are different tools available for Data Analysis?

There are multiple IDEs available which allows you to do the same and also there are multiple languages by which you can program things.

Example IDEs to work with:

  1. Jupyter Notebook
  2. R Studio
  3. SAS Tool
  4. Spyder
  5. PyCharm
  6. Microsoft Visual Studio

Example Languages to work with:

  • Python
  • R
  • Java Script

How do we do it?

Data Analysis is a crucial part of any project. This is where we explore our data, where we understand the patterns in data. It is carried out through the below steps:

  1. Finding Measures of Central Tendency
  2. Finding Measures of Variability or Dispersion
  3. Checking the Distribution Types
  4. Performing Graphical Visualizations
  5. Creating Summary Tables

There is a detailed section for Data analysis in the Statistics track. It is recommended to go through those articles first and understand how we can perform data analysis.

There can be cases where your Data is not cleaned, it contains some junk values or it has some inconsistencies, then there is a need to perform the Data Cleaning part first and once data is cleaned, then you can proceed with your Data analysis part.

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