Understanding EDA
EDA plays a pivotal role in the data analysis workflow, serving as an essential phase where the dataset undergoes initial scrutiny and exploration. Its primary purpose is to summarize the main characteristics of the data, uncover patterns, identify potential outliers, and gain insights into the data’s underlying structure.
Well-carried-out EDA when first connecting to data provides the following benefits:
- It helps analysts and data scientists familiarize themselves with the dataset they are working with. It provides an overview of the data’s scope, size, and complexity, allowing them to assess its suitability for their analysis objectives.
- Data quality issues such as missing values, duplicates, and inconsistencies are often identified.
- It reveals patterns, trends, and relationships within the data. Analysts use various graphical and statistical techniques to visualize data distributions, correlations, and outliers.
- It also helps...
