Exploratory Data Analysis

Exploratory data analysis (EDA) is a crucial initial step in data science, aiming to uncover patterns, anomalies, and insights within datasets before formal modeling. Current research emphasizes automating EDA processes, particularly for non-experts, through tools incorporating techniques like topic modeling, dimensionality reduction (e.g., t-SNE, UMAP), and machine learning algorithms (e.g., k-means, DBSCAN, Isolation Forest) for tasks such as clustering, anomaly detection, and visualization. This focus on automation and explainability enhances accessibility and facilitates more efficient and insightful data exploration across diverse fields, from social sciences and business analytics to environmental studies and financial modeling.