Exploratory Data Analysis (EDA) is a crucial step in data science projects. It allows data scientists to understand their data, reveal patterns, and detect abnormalities. EDA involves computing basic descriptive statistics, handling null values, and correcting inconsistencies. The process also includes data visualization methods such as histograms, box plots, scatter plots, and bar charts.

Correlation analysis can help identify relationships between variables, while outlier detection is essential for ensuring the integrity of the data. Z-Score Method and Interquartile Range (IQR) are two common approaches to identifying outliers.

In conclusion, EDA is a fundamental step in data science that enables data scientists to gain insights into their data. It prepares the foundation for subsequent analysis and helps develop skills in data science.

Source: https://dev.to/nderitugichuki/understanding-your-data-the-essentials-of-exploratory-data-analysis-400i