Exploratory Data Analysis (EDA)

Exploratory Data Analysis (EDA) is the initial step in the data analysis process. It is the process of studying and summarizing the main characteristics of the data, usually done with visual methods. EDA is used to understand the data, identify patterns and anomalies, and check for assumptions. The aim of EDA is to get a feel for the data and to develop hypotheses that can be tested in subsequent analyses.

Techniques used in EDA

There are several techniques used in EDA, including:

• Univariate analysis
• Bivariate analysis
• Multivariate analysis

In univariate analysis, one variable is studied at a time, and the goal is to look at the distribution of that variable. In bivariate analysis, two variables are studied together, and the goal is to understand the relationship between them. Multivariate analysis looks at multiple variables simultaneously.

Visualization

One common technique used in EDA is visualization. Visualization can help to identify patterns and anomalies in the data. Histograms, scatter plots, and box plots are some common visualization techniques used in EDA. For example, a histogram can be used to visualize the distribution of a variable, while a scatter plot can be used to visualize the relationship between two variables.

Data cleaning

Another important aspect of EDA is data cleaning. Data cleaning involves identifying and correcting errors in the data. This is important because errors in the data can lead to incorrect conclusions.

In summary, EDA is an essential step in the data analysis process that involves studying and summarizing the main characteristics of the data. It includes techniques such as univariate analysis, bivariate analysis, and multivariate analysis, as well as visualization and data cleaning.

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