Biomedical datasets range from genetic sequences to clinical trial data and medical imaging, each possessing unique characteristics, formats, and challenges. The process of data exploration is the first step in any data analysis pipeline. It serves as the initial means of understanding and interpreting the large, complex datasets. During this exploratory phase, researchers examine the structure and quality of the data, typically by summarizing the dataset with descriptive statistics, visualizing distributions and relationships with graphs and charts, and assessing the quality of the data by identifying missing values, outliers, or inconsistencies. This systematic exploration also helps to identify patterns and anomalies and ensures that the data is suitable for further, more advanced analysis.