The module is structured into two core components: Probability Theory and Statistics. In Probability Theory, we embark from foundational concepts, delving into random events, probability spaces, conditional probability, random variables and their distributions, multivariate random variables, and limit theorems. You will master the assessment of the likelihood of uncertain events, comprehend the relationships and variations among random variables, laying a solid foundation for subsequent statistical analysis.

The Statistics section focuses on data collection, organization, description, inference, and prediction. We will learn how to design effective sampling schemes, use graphical representations (histograms, box plots, scatter plots, etc.) and numerical measures (mean, median, mode, variance, etc.) to initially describe data. Furthermore, through statistical inference methods such as parameter estimation and hypothesis testing, we extract valuable information about the population from sample data. Lastly, advanced statistical techniques like regression analysis and analysis of variance are introduced to explore correlations and influencing mechanisms among variables, providing scientific evidence for decision-making.