.

Similarly, it is asked, why do we do data exploration?

Data exploration is the initial step in data analysis, where users explore a large data set in an unstructured way to uncover initial patterns, characteristics, and points of interest. More importantly, it helps build a familiarity with the existing information that makes finding better answers much simpler.

Furthermore, why is exploratory data analysis important? Exploratory Data Analysis is a critical component of any analysis they serve the purpose of: Get an overall view of the data. Focus on describing our sample – the actual data we observe – as opposed to making inference about some larger population or prediction about future data to be collected.

Furthermore, what does data exploration mean?

Data exploration is an approach similar to initial data analysis, whereby a data analyst uses visual exploration to understand what is in a dataset and the characteristics of the data, rather than through traditional data management systems.

What are the steps of the process of data exploration?

The steps for data exploration are in this order:

• Variable Identification:
• Univariate Analysis:
• Bi-Variable Analysis:
• Detecting / Treating missing values.
• Detecting / Treating outliers:
• Feature Engineering: