Numerical Methods for Chemical Engineering: Linear Least Squares

Analyzing experimental data is a core skill for chemical engineers, yet translating raw laboratory measurements into accurate mathematical models can be challenging. This course bridges the gap by introducing you to the fundamental numerical methods used to fit data and model chemical processes. You will progress from understanding the basic math behind linear least squares to implementing data fitting workflows. You will learn how to structure your engineering data, apply regression techniques, and interpret the results to make reliable predictions about chemical systems. What you'll learn: - Understand the foundational concepts of linear least squares and regression analysis in chemical engineering - Apply modern programming tools and libraries to clean and organize experimental data - Formulate mathematical models for reaction kinetics, thermodynamics, and transport phenomena - Solve linear regression problems and calculate parameter uncertainties to assess model reliability - Implement modern data workflows using structured, readable code practices The course begins with key terminology and foundational definitions before moving into practical data-fitting exercises. You will read through clear explanations and step-by-step code examples that demonstrate how to solve typical chemical engineering problems. This course is designed for beginners, with no advanced programming or prior numerical analysis experience required. Start mastering the numerical methods that turn raw engineering data into actionable insights.