Before building models or exploring relationships between variables, it’s important to understand each variable independently. This process is called univariate analysis, which examines one column at a time to understand its distribution, central tendency, variability, and any unusual values.

You looked at your dataset's structure and data types in earlier steps. Now, you’ll dig deeper into each variable to understand what it can tell you. You’ll look at its typical values, how much it varies, and whether it contains any outliers or patterns that might affect your later analysis.

In this lesson, you’ll learn how to apply univariate statistics to explore individual columns. You’ll measure center (mean, median), spread (range, standard deviation), and shape (skewness, kurtosis) to build a clearer picture of your data, one variable at a time.

What is univariate analysis?

Univariate analysis is simply the examination of a single variable in isolation. It’s about understanding its distribution, central tendency (the typical value), and variability (how much values differ). At this stage, we’re not concerned with relationships or interactions with other variables—just the characteristics of one variable by itself.

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