A Guide to the Most Common Exploratory Data Analysis Tools

Exploratory data analysis (EDA) is a broad umbrella of methods and techniques that help you understand your data before you begin your analysis. It's not just about looking at numbers, but also trying to understand what those numbers are saying and whether they are saying something useful. Exploratory data analysis requires a balance of theory, intuition and practice to succeed with it. There are many tools, techniques and methods for carrying out EDA. Each of them has its own benefits and trade-offs. Let’s look at the most common EDA tools, types and techniques in this article.

What is Exploratory Data Analysis?

Exploratory data analysis (EDA) is the process of analyzing data to understand what is happening within the data, without necessarily having a particular end goal or prediction in mind. EDA is a broad concept that encompasses different data analysis techniques, including descriptive statistics, univariate analysis, data visualization, and data transformation. EDA is often a precursor to the more formal analysis needed to answer a research question. Data analysts use EDA to explore the data, identify patterns, and create visual representations of their findings. The goal of EDA is to gain an understanding of the data that can then inform the rest of the data analysis process.

Why do Exploratory Data Analysis?

There are many reasons to conduct exploratory data analysis (EDA). You might be given a data set that you have not worked with before, and don’t know what it means or how to start your analysis. Or, maybe the data set has an important limitation or gap in it, which you need to know about. So, you may need to confirm that your data is correct and complete, or perhaps test its robustness against outliers. Whatever your reason for EDA, it is important to be clear about what you hope to accomplish from it. That way, you will be able to decide which EDA methods and techniques are most appropriate for your data.

Types of Exploratory Data Analysis 

EDA methods can be categorized in terms of their goals, their approach, and how they relate to one another. Here is a brief overview of the most common types of EDA: -

●Summarization and data cleansing: The first important step in EDA is to summarize your data, and then to clean it if necessary. This can involve calculating descriptive statistics like the mean, median, mode, or standard deviation, or other metrics relevant to your data set. You might also explore ways to transform or recode your categorical variables to make them more suitable for analysis. This part of EDA is important because it helps you confirm whether you have a complete and valid data set, and that you have useful information about your data that can inform your EDA going forward.
● Visualization: Once you have summarized and cleaned your data, the next step is to visualize it. This can take many forms, such as creating a histogram or distribution plot for your quantitative data, or mapping your qualitative data. Visualization helps you to understand what is going on within your data and makes it easy to potential issues.
● Dimensionality reduction: This is a technique for getting a better understanding of your data by finding the underlying structure, or “hidden patterns”. It can be used on either qualitative or quantitative data, but is often applied to categorical variables. Examples of this include, principal component analysis (PCA) and cluster analysis.
● Univariate analysis: This process involves an analysis of one variable at a time. It can help you answer questions like, “What is the relationship between age and income?” or “What are the most common words used in customer reviews?” Univariate analysis is widely used in EDA, and is often applied to both quantitative and categorical data.
● Probing questions and confounding variables: Another important aspect of EDA is probing questions and testing for potential confounding variables. This is a process of asking questions about your data, and then testing hypotheses using the data itself. This can range from looking at the distribution of values within your data, to testing for differences between subgroups such as males and females. There is no hard and fast rule for what makes up a probing question or a confounding variable. Instead, it is something that can only be determined by the data analyst.


Exploratory data analysis is an important part of the data analysis process. It helps you better understand your data, spot issues, and ask questions about it. EDA is not just about looking at numbers, but about trying to understand what those numbers are saying and whether they are saying something useful. It requires a balance of theory and intuition.

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