Exploratory Data Analysis

Definition of Exploratory Data Analysis

Exploratory Data Analysis: Exploratory data analysis (EDA) is the examination of data to summarize, visualize, and discover patterns. EDA is used to identify which variables are important and to develop hypotheses about the relationships between variables.

What is an Exploratory Data Analysis used for?

An Exploratory Data Analysis (EDA) is a type of data analysis used to explore, summarize, and visualize data. It is often used as the first step in exploring a dataset before more detailed statistical analysis. Through EDA, patterns and relationships between variables can be discovered and further investigated. This helps to identify potential problems with the data such as outliers or inconsistencies.

EDA also involves summarizing the characteristics of a dataset by examining its distributions, correlations, and other measures of central tendency such as mean, median, mode, standard deviation, etc. By visualizing the data in multiple ways such as bar graphs and line plots, it can become easier to spot trends or patterns that may have otherwise gone unnoticed. Additionally, using clustering techniques or other machine learning algorithms can help uncover useful insights from datasets that cannot easily be detected through manual exploration.

Ultimately, Exploratory Data Analysis serves as an essential tool for understanding a dataset’s structure and content before drawing conclusions from it or making assumptions about it. It’s also especially useful for identifying areas where further investigation is needed in order to gain better insight into a dataset’s features or behavior.