EXPLORATORY DATA ANALYSIS AND VISUALIZATION Article Swipe
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· 2025
· Open Access
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· DOI: https://doi.org/10.58532/nbennuraith5
· OA: W4411840299
Exploratory Data Analysis (EDA) is a foundational step in the data science process, enabling practitioners to uncover meaningful patterns, detect anomalies, and inform subsequent modeling decisions [1, 2]. By systematically visualizing and summarizing data, EDA reveals relationships, trends, and data quality issues that might otherwise remain hidden. Tools such as Matplotlib and Seaborn in Python, along with Tableau for interactive dashboards, empower analysts to create compelling visualizations that clarify complex datasets and communicate insights effectively. EDA plays a pivotal role in anomaly detection, guiding data cleaning and feature engineering by highlighting outliers and inconsistencies. Interactive dashboards further enhance the EDA process by allowing users to explore data dynamically and tailor analyses to specific business questions. Ultimately, EDA ensures that data-driven decisions are based on a deep under- standing of the underlying data, leading to more robust models and actionable insights. This chapter explores EDA techniques, visualization tools, and best practices for extracting value from raw data.
