Exploratory Data Analysis: Step by Step

Before we start, let us first understand what exactly is EDA and why is it important?

Exploratory Data Analysis (EDA) is an approach to analyzing and understanding data sets through visualizations and statistical techniques.

It is a crucial step in the data science processes as it allows analysts to:

Gain a deeper understanding of the data they are working with
Identify patterns and correlations
Identify outliers that may need to be addressed before building models or making predictions.

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By performing EDA, analysts can make informed decisions about the data and identify any areas that require further exploration. Additionally, EDA is an iterative process, it can be done multiple times to dig deeper into the data and make sure that the final model is robust and accurate.

1. Define the problem and gather the data:

The first step in the exploratory data analysis process is to clearly define the question or problem you want to investigate. This will guide the data collection process and ensure that you are gathering the necessary data to answer your question. Once you have defined the problem, you can gather the relevant data from various sources such as databases, spreadsheets, or external data sets.

2. Clean and organize the data:

Once you have collected the data, it is important to clean and organize it to ensure that it is ready for analysis. This step involves removing any missing or irrelevant data, correcting any errors, and organizing the data in a way that makes it easy to work with. This might include sorting the data, creating new columns, or merging multiple data sets.

3. Univariate Analysis:

This step involves analyzing each variable individually to understand the distribution of the data and identify any outliers or anomalies. You can use statistical measures such as mean, median, mode, and standard deviation to describe the data, and visualizations such as histograms, box plots, and scatter plots to help you understand the distribution of the data.

4. Bivariate Analysis:

Bivariate analysis allows you to analyze the relationship between pairs of variables. This step is useful for identifying patterns or correlations in the data. You can use visualizations such as scatter plots, line plots, and bar charts to help you understand the relationship between variables.

5. Multivariate Analysis:

Multivariate analysis allows you to analyze the relationship among multiple variables. This step can help you gain a deeper understanding of the data and identify any trends or patterns. You can use techniques such as principal component analysis, factor analysis, and cluster analysis to conduct multivariate analysis.