Data Analytics for Chemical Engineers

Catalog description

This course introduces students to the principles and tools of data analytics and predictive modeling, tailored to chemical engineering applications. Topics include data acquisition and cleaning, data management, exploratory data analysis, probability distributions, hypothesis testing, propagation of error, regression modeling, and design of experiments. Students will also gain exposure to multivariate analysis, process monitoring, data visualization, machine learning, and the application of artificial intelligence (AI) tools in engineering analysis. Emphasis is placed on applying analytical methods to real-world engineering data using modern computational platforms such as Python, R, or MATLAB. By the end of the course, students will be able to manage and interpret complex datasets, quantify uncertainty, and support data-driven decision-making in chemical process design, optimization, and troubleshooting. Prerequisites: MATH 185 , MATH 186 and CHML 207 .