Abstract:
Exploratory Data Analysis (EDA) provides a set of simple tools to achieve a basic understanding of a dataset. The results of data exploration are very useful in understanding connections and the structure of the dataset, the distribution of the values, the presence of extreme values, and interrelationships within the dataset. In Data Analytics, EDA is the first or main step used to understand, evaluate, and visualize data to obtain important information from the beginning or to identify patterns or key areas that you can dig deeper. It uses a combination of automated tools and manual methods such as charts, visuals, and reports to help clarify and comprehend datasets. With EDA we get a lot of details from the data, reveal its basic structure, detect any external, error data, and confusion if there is data, evaluate the basic assumptions, and determine the appropriate feature settings. Using data exploration tools and methods such as dashboards, reports, and point-to-point datasets users can understand the big picture and can find information on it easily.
Description:
Data is often collected in vast and unstructured formats and stored in different data types such as structured, unstructured, and semi-structured from various sources, it becomes necessary for data analysts to first view, understand, and develop a comprehensive view of the data before extraction of the data for further analysis.
