Exploratory Data Analysis (EDA) in Python using SQL and Seaborn (SNS)

Exploratory Data Analysis (EDA) is an approach of analyzing datasets to summarize their main characteristics, often using statistical graphics and other data visualization methods. Various statistical models can be used or not, but primarily EDA is used for seeing what the data can tell us beyond the formal modeling or hypothesis testing task.

Guess what…always…

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Why should I do any EDA in the first place?

I believe a more suitable question would be:

In which case shouldn’t I use EDA?

EDA is one of the crucial step in Data Science that allows us to achieve certain insights and statistical measure of the data we are dealing with. That is essential for an endless plethora of users, including business managers, stakeholders, data scientists etc.

For data scientists, EDA helps to define and refine our important features variable selection, that will be used in the yet-to-train Machine Learning model.

In this story, for demonstration purposes, we will use some FitBit data.

Fitness Trackers data are a popular area of study amongst data scientists, statisticians, medical experts, physiologists, and psychologists, just to name a…