Welcome to the official repository for CHE597: Data Science in Chemical Engineering at Purdue University.
About the Course¶
CHE597 introduces data science concepts and tools for chemical engineering applications. The course covers Python programming, data analysis, machine learning, optimization, and modern computational workflows, with hands-on lessons and real-world datasets.
Instructor: David E. Bernal Neira
Davidson School of Chemical Engineering, Purdue University
Principal Investigator, SECQUOIA
Jupyter Book¶
All lessons and materials are organized as a Jupyter Book, including:
19 interactive Jupyter notebooks (one per lesson)
Example datasets and code
Direct launch to Google Colab for each notebook
View the book online: CHE597 Jupyter Book (GitHub Pages)
How to Use¶
Browse lessons and code in the Jupyter Book (see sidebar for topics)
Click the “Open in Colab” button at the top of any notebook page to run interactively in Google Colab
Download notebooks and data for local use
About SECQUOIA¶
SECQUOIA is an initiative to advance open, reproducible, and accessible computational science in chemical engineering and related fields. This course and its materials are part of SECQUOIA’s mission to empower students and researchers with modern data science skills.
© 2026 David E. Bernal Neira, Purdue University. Built with Jupyter Book.