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Exploratory Data Analysis (EDA) is the critical first step in any data science or analytics project. It involves understanding the structure, trends, and patterns within a dataset before applying advanced analytical methods. With effective visualizations, EDA becomes more insightful and intuitive, enabling data scientists to make informed decisions.
This blog will cover the essentials of EDA and demonstrate how to use Python for creating impactful visualizations.
What is Exploratory Data Analysis?
EDA is the process of analyzing datasets to summarize their main characteristics, often using visual methods. The primary goals of EDA are:
- Understanding Data Distribution: Identifying central tendencies, spread, and outliers.
- Detecting Patterns and Relationships: Understanding how variables interact.
- Identifying Data Quality Issues: Spotting missing values or inconsistencies.
Why Use Visualizations in EDA?
- Simplifies Complexity: Visualizations help in making sense of high-dimensional data.
- Reveals Hidden Patterns: Graphs can uncover trends that raw data may not show.
- Facilitates Communication…
