Probability and Statistics for Civil and Environmental Engineers

Every engineering decision involves uncertainty, from predicting peak flood levels to assessing the structural integrity of a bridge. Understanding how to quantify this uncertainty using probability and statistics is essential for designing safe, resilient infrastructure. This course guides you from the fundamental concepts of risk and probability to the practical statistical models used in civil and environmental engineering today. You will start with core terminology, learning how to define sample spaces, calculate probabilities, and work with random variables and vectors. Through clear, written explanations and structured engineering scenarios, you will build the analytical skills needed to model complex environmental systems and evaluate structural reliability. What you'll learn: - Understand fundamental probability concepts, sample spaces, and engineering risk parameters - Model engineering uncertainty using discrete and continuous probability distributions - Analyze joint behavior in systems using random vectors and covariance - Apply statistical estimation techniques to environmental and structural data sets - Evaluate extreme event probabilities for flood, wind, and seismic design calculations - Interpret modern statistical outputs to make informed, risk-aware engineering decisions This course begins with basic probability definitions before moving into discrete and continuous distributions, joint variables, and engineering applications. You will read comprehensive breakdowns of mathematical models and practice applying them to realistic engineering scenarios. This course is designed for undergraduate engineering students, practicing civil and environmental engineers, and anyone looking for a solid foundation in engineering statistics. No prior background in advanced probability is required, though a basic understanding of calculus is helpful. Start building your engineering analytical toolkit and master the science of uncertainty today.