Having explored the theoretical foundations of probability, we now turn to the practical task of understanding datasets. Raw data often requires initial summarization to reveal its core characteristics before more complex analysis or modeling. This chapter introduces descriptive statistics – techniques for quantitatively describing the main features of a collection of data.

You will learn to compute and interpret key summary measures:

• Central Tendency: Find the 'center' of your data using the mean, median, and mode.
• Dispersion: Quantify the spread or variability using variance ($\sigma^2$), standard deviation ($\sigma$), and range.
• Shape: Describe the asymmetry (skewness) and peakedness (kurtosis) of the data distribution.
• Position: Understand relative standing within the data using percentiles and quartiles.
• Association: Measure linear relationships between variables using correlation coefficients.

We will also emphasize the important distinction between correlation and causation and demonstrate how to calculate these statistics efficiently using the Pandas library in Python, alongside visualization techniques like histograms and box plots. By the end of this chapter, you'll be equipped to effectively summarize and communicate the essential properties of datasets.