Numerical Methods and Probability for Chemical Engineers

Chemical engineering relies heavily on interpreting experimental data and predicting system behaviors under uncertainty. This text-based course guides you through the fundamental numerical methods and probability theories essential for analyzing chemical systems and validating models against real-world data. You will transition from theoretical equations to confidently fitting models and estimating parameters with statistical rigor. What you'll learn: Understand core probability concepts and statistical distributions relevant to chemical systems; Apply numerical methods to solve algebraic and differential engineering equations; Compare experimental data against mathematical models using regression techniques; Implement parameter estimation and analyze uncertainty in your engineering calculations; Practice modern data handling workflows using structured numerical libraries. This course begins with foundational definitions of probability and numerical approximation before moving into practical engineering applications and data modeling workflows. It is designed specifically for beginners, students, and practicing engineers looking for a solid mathematical refresher, with no advanced prerequisites required. Start mastering the mathematical tools that drive modern chemical engineering analysis.