Exploratory Data Analysis

In the previous chapter, we discussed what an ETL pipeline is, its importance, and how to implement it. What do we do next once we load this data in its desired location? Sometimes, you can have so much data that it may be difficult to understand where to start or finish. It may be difficult to see at first how this data can help your organization. Imagine inheriting a treasure chest from the past with numerous journals. Each journal has stories, cryptic symbols, and maps. As you look through this treasure, you realize the key to unraveling the secrets held within this chest is understanding and connecting the hidden patterns you find. Similarly, exploratory data analysis is like going through a treasure trove of data – each dataset is a collection of numbers, variables, and observations. Just as you would carefully examine the journals to decipher their contents to piece together the narrative of their originating authors, exploratory data analysis involves pulling back the layers to obtain insights, trends, and anomalies lurking hidden beneath the surface.