With data gathered and prepared, the next step is to begin extracting insights. This chapter introduces foundational techniques for analyzing datasets.

You will learn how to initiate Exploratory Data Analysis (EDA) to understand your data's main characteristics. We will cover methods for calculating and interpreting summary statistics, including measures of central tendency like the mean, median ($M_e$), and mode, and measures of spread such as range, variance ($s^2$), and standard deviation ($\sigma$).

Further sections explain frequency distributions and their role in describing data patterns. A key concept discussed is the distinction between correlation and causation, a common point of confusion in analysis. You will also get a conceptual introduction to forming and testing hypotheses. The chapter concludes with a hands-on practical session focused on calculating these basic statistics.