# What are types of EDA? ###### Author

Hi there! Today, we’ll be talking about the different types of exploratory data analysis (EDA). EDA is a way of exploring data through visual summaries and graphics, and there are several different types of EDA to choose from.

The three main types of EDA are univariate, bivariate, and multivariate EDA. Let’s break down what each of these means:

Univariate EDA involves looking at a single variable at a time. Univariate EDA can help you understand the distribution of the data and identify any outliers.

Bivariate EDA involves looking at two variables at a time. Bivariate EDA can help you understand the relationship between two variables and identify any patterns that might exist.

Multivariate EDA involves looking at three or more variables at a time. Multivariate EDA can help you understand the relationships between several variables and identify any complex patterns or outliers that might exist.

Within each of these types of EDA, there are both graphical and non-graphical methods. Graphical methods involve creating charts, graphs, and other visualizations to explore the data, while non-graphical methods involve using statistical techniques to explore the data.

Univariate graphical EDA involves creating charts and graphs to explore a single variable. This can help you understand the distribution of the data and identify any outliers.

Univariate non-graphical EDA involves using statistical techniques to explore a single variable. This can include measures of central tendency (like the mean or median), measures of spread (like the range or standard deviation), and measures of shape (like skewness or kurtosis).

Multivariate graphical EDA involves creating charts and graphs to explore three or more variables at a time. This can help you understand the relationships between several variables and identify any complex patterns or outliers.

Multivariate non-graphical EDA involves using statistical techniques to explore three or more variables at a time. This can include techniques like regression analysis or principal component analysis.

In conclusion, there are several different types of exploratory data analysis, including univariate, bivariate, and multivariate EDA. Within each of these types, there are both graphical and non-graphical methods for exploring the data. By using a combination of these techniques, you can gain a better understanding of your data and make more informed decisions when analyzing it. So go forth and explore your data with EDA!