Data Exploration

Data exploration is the process of analyzing and summarizing a dataset to get new insights and a deeper understanding of its properties, trends, relationships, and any outliers. It is a crucial phase in the process of data analysis that identifies any errors with the data, such as missing values, duplicates, and outliers, and provides a foundation for choosing the most suitable statistical and machine learning algorithms for further analysis. In other words, it assists in identifying data issues. In the process of data exploration, visualizations, descriptive statistics, and hypothesis testing are all viable strategies. The goal of data exploration is to uncover hidden patterns and correlations within the data, get a knowledge of the data's distribution, and identify any potential obstacles that may impact the future analysis and interpretation of the results.