Exploratory Data Analysis (EDA) is a fundamental and vital phase in a data science project. A data scientist spends about 70% of their time on the EDA process. In this article, we will explain what EDA is and outline the steps involved in performing it.
What is Exploratory Data Analysis (EDA)?
Exploratory Data Analysis (EDA) is a critical first step in any data science project. It involves analyzing and visualizing data to understand its main features, uncover patterns, and identify relationships among variables. Essentially, it’s the method of examining datasets to understand their key traits, spot trends, identify outliers, and explore variable connections. Typically, EDA is performed before diving into more formal statistical analysis or modeling.
Why is Exploratory Data Analysis Important?
EDA plays a crucial role for several reasons, particularly in the context of data science and statistical modeling.
Types of Exploratory Data Analysis
EDA refers to the process of analyzing and examining datasets to uncover patterns, detect relationships, and gain insights. Different EDA techniques can be applied depending on the data’s nature and the analysis objectives.
Steps for Performing Exploratory Data Analysis
The process of conducting EDA involves several key steps aimed at understanding the data, discovering patterns, spotting anomalies, testing hypotheses, and ensuring the data is clean and ready for further analysis.
Conclusion
Exploratory Data Analysis is the foundation of data science projects, providing valuable insights into the characteristics of datasets and setting the stage for informed decision-making. By exploring data distributions, relationships, and anomalies, EDA helps data scientists uncover hidden patterns and guide projects toward successful outcomes.
