This course provides a comprehensive introduction to Exploratory Data Analysis (EDA) — the essential first step in any data science or machine learning project.

Students will learn how to inspect, clean, transform, and visualize datasets to uncover meaningful patterns, detect anomalies, test assumptions, and generate insights that guide further analysis or modeling.

Through a combination of theory, practical exercises, and real-world datasets, learners will develop the skills to summarize data effectively, select appropriate visualization techniques, and make data-driven decisions with confidence.

Key topics include:

Understanding data types and distributions
Handling missing values and outliers
Statistical summaries and descriptive analysis
Data visualization and pattern recognition
Correlation analysis and feature relationships
Communicating findings effectively