Exploratory Data Analysis (EDA) is an important step in the process of analyzing data sets to understand and summarize their main characteristics. It involves the use of statistical graphics and visualization techniques to identify general patterns in the data and uncover any unexpected features or outliers. EDA is often employed by data scientists to gain insights and make informed decisions based on the data.
According to the Knowledge Graph from Wikipedia, exploratory data analysis is a statistical approach that involves summarizing key characteristics of data sets using statistical graphics and other visualization methods. This approach helps in understanding the data and identifying any important patterns or trends that may be present.
The US EPA also provides information on exploratory data analysis, stating that it is an analysis approach that aims to identify general patterns in the data. This includes identifying outliers and any unexpected features that may be present in the data. EDA is considered an important first step in any data analysis process.
Another article from Towards Data Science emphasizes the critical process of performing initial investigations on data in order to discover patterns and spot any anomalies or interesting insights. Exploratory Data Analysis is seen as a key tool in this process, helping to uncover hidden insights and understand the underlying structure of the data.
IBM, in their article on exploratory data analysis, mentions that data scientists use EDA to analyze and investigate data sets, summarizing the main characteristics. This is achieved through the use of various techniques and methods, including visualization, to gain a deeper understanding of the data.
Overall, exploratory data analysis plays a crucial role in understanding and summarizing data sets. By employing statistical graphics, visualization techniques, and other tools, data scientists are able to uncover patterns, identify outliers, and gain valuable insights from the data. EDA is considered an essential step in the data analysis process, providing a foundation for further analysis and decision-making.