Exploratory data analysis (EDA) is a statistical method that helps you understand your data by summarizing its main characteristics using statistical graphics and other data visualization methods. It is conducted to discover patterns, spot anomalies, test hypotheses, and check assumptions. EDA is typically the first step in data analysis, and it can be used to inform subsequent data modeling and hypothesis testing.
There are many different EDA techniques, but some of the most common include:
- Univariate analysis: This involves summarizing the distribution of a single variable. For example, you might calculate the mean, median, and standard deviation of a variable, or you might create a histogram or boxplot to visualize its distribution.
- Bivariate analysis: This involves exploring the relationship between two variables. For example, you might create a scatterplot to see if there is a linear relationship between two variables, or you might calculate the correlation coefficient to measure the strength of the relationship.
- Multivariate analysis: This involves exploring the relationship between multiple variables. For example, you might create a heatmap to visualize the relationship between three variables, or you might use a multivariate statistical test to determine if there is a significant relationship between multiple variables.
EDA is a powerful tool that can help you gain a deeper understanding of your data. By using EDA techniques, you can identify patterns, spot anomalies, test hypotheses, and check assumptions. This information can be used to inform subsequent data modeling and hypothesis testing, and it can help you make better decisions about your data.
Here are some of the benefits of using EDA:
- It can help you understand your data better.
- It can help you identify patterns in the data.
- It can help you make predictions about the data.
- It can help you communicate the results of your data analysis to stakeholders.
Here are some of the limitations of using EDA:
- It can be time-consuming and labor-intensive.
- It can be difficult to interpret the results of EDA if you do not have a strong understanding of statistics.
- EDA can only tell you what is in the data, not why it is there.
