Exploratory Data Analysis (EDA) involves a variety of data visualization methods to understand the underlying patterns and relationships in a dataset. Key techniques include:

1. Histograms: These display the distribution of a single variable by showing the frequency of data points within specified ranges, helping identify patterns like skewness and the presence of outliers.

2. Box Plots: Box plots (or box-and-whisker plots) provide a visual summary of the distribution of a variable by showing its median, quartiles, and potential outliers, which helps in understanding the spread and central tendency.

3. Scatter Plots: These are used to investigate the relationship between two continuous variables, revealing correlations, clusters, and potential outliers.

4. Bar Charts: Bar charts represent categorical data with rectangular bars, showing the frequency or proportion of each category, which is useful for comparing sizes and identifying patterns across categories.

5. Pie Charts: Although less common in rigorous analysis, pie charts can show the proportions of categories in a dataset, giving a quick sense of how different categories compare to each other.

6. Heatmaps: Heatmaps use color to represent the values in a matrix, making it easy to identify patterns, correlations, and anomalies in a dataset, especially in relation to time or other variables.

7. Pair Plots: Also known as scatterplot matrices, pair plots show scatter plots for all pairs of variables in a dataset, allowing for an overview of relationships and distributions across multiple dimensions.