Exploratory Data Analysis (EDA) is the stage where you look closely at a dataset to understand what it contains, what seems unusual, and what questions it can realistically answer. In this lesson, learners focus on the purpose of EDA: to reveal structure, spot data quality issues, compare variables, and form useful hypotheses before any formal modeling.
By the end of the lesson, learners should understand EDA as a practical workflow, not a fixed checklist. They should be able to distinguish EDA from data cleaning and statistical modeling, explain why visuals and summary statistics work best together, and identify the main kinds of insights EDA can surface. Advanced techniques, coding details, and model-building come later in the course.
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