Data Science and its subfields can demoralize you at the initial stage. The reason is that understanding the transitions in statistics, programming skills (like R, Python), and algorithms (whether supervised or unsupervised) are tough to remember as well as implement. Are you planning to leave this battle without fighting thinking you are just a beginner? This will make the situation more complicated and to rescue yourself, what you should be doing is gaining some hands-on experience by doing projects & solving real-time problems speedily and profitably. Data exploration refers to the initial step in data analysis in which data analysts use data visualization and statistical techniques to describe dataset characterizations, such as size, quantity, and accuracy, in order to better understand the nature of the data.
Data exploration techniques include both manual analysis and automated data exploration software solutions that visually explore and identify relationships between different data variables, the structure of the dataset, the presence of outliers, and the distribution of data values in order to reveal patterns and points of interest, enabling data analysts to gain greater insight into the raw data.
Data is often gathered in large, unstructured volumes from various sources and data analysts must first understand and develop a comprehensive view of the data before extracting relevant data for further analysis, such as univariate, bivariate, multivariate, and principal components analysis.