Exploratory Data Analysis (EDA) is an approach to analyzing and understanding datasets by visually and statistically summarizing their main characteristics, often with the help of graphical representations. The goal of EDA is to uncover patterns, relationships, outliers, and anomalies in the data, and to check assumptions before applying more formal modeling techniques.
Key activities in EDA include:
- Data Cleaning: Identifying and handling missing, incorrect, or outlier values.
- Data Visualization: Using graphs like histograms, scatter plots, box plots, and heatmaps to visualize data distribution, relationships, and trends.
- Descriptive Statistics: Calculating summary statistics such as mean, median, mode, variance, and correlation coefficients to quantify data characteristics.
- Detecting Patterns: Identifying correlations or trends within the data and testing hypotheses or assumptions.
- Identifying Outliers: Using statistical tests or visual methods to detect data points that significantly differ from the rest of the dataset.
EDA is an essential step in the data analysis process because it helps inform the direction of further analysis or modeling and ensures a deeper understanding of the data before any complex techniques are applied.