Chemical Engineering + Data Science: An Integrated Degree for the Age of AI

From mass balances to machine learning, a new ChemE+DS degree fuses chemical engineering and data science. Graduates will be equally comfortable running reactors and writing algorithms, making them standouts in the future workforce.

A new “Chemical Engineering + Data Science” (ChemE+DS) undergraduate degree at the Univ. of Illinois includes a complete core sequence in chemical engineering along with courses in computer science, mathematics, statistics, ethics, and data management. Four additional courses at the interface of the two disciplines give students the opportunity to learn and use data science techniques in the specific context of chemical engineering.

This integrated approach prepares graduates for emerging opportunities in data analytics and machine learning across the chemical process industries. The program is designed not only to meet evolving workforce demands but also to help shape the future of chemical engineering education.

Introduction

The traditional chemical engineering curriculum covers mass and energy balances, thermodynamics, transport phenomena, reaction kinetics, unit operations, and process control, all combined with design and economic analyses. In a literal sense, the traditional curriculum built the modern world through the development and production of fuels, chemicals, plastics, fertilizers, processed foods, semiconductors, etc. (1).

Today, a profusion of data, machine learning, and artificial intelligence (AI) tools is transforming every aspect of our lives. Major changes are coming to chemical engineering as well (2–6). Data-driven models are increasingly used in process engineering for control and optimization, process development, and automated experimentation, as well as in product development for chemical property prediction and materials discovery (7–11).

Like many departments, Chemical and Biomolecular Engineering at the Univ. of Illinois at Urbana-Champaign is working to integrate data science and AI tools in our curriculum. We instituted a probability and statistics requirement long before the surge of interest in AI, but a modern data science training must go beyond an introduction to probability and statistics. This article provides our perspective on essential components of a joint chemical engineering and data science degree, challenges in adding data science courses to an already rigorous chemical engineering curriculum, and departmental and institutional resources that helped us implement the additional coursework.

The Univ. of Illinois launched a campus-wide initiative to facilitate the creation of cross-disciplinary X + Data Science (X+DS) degrees. Here, X indicates a domain expertise area, and DS indicates a data science curriculum that includes courses in math, statistics, computer science, ethics, and information sciences. As part of the X+DS initiative, we created ChemE+DS, a first-of-a-kind cohesively blended undergraduate degree in engineering and data science (Figure 1).

---

▲Figure 1. The Chemical Engineering + Data Science (ChemE+DS) program combines a traditional core sequence in chemical engineering with courses in programming, statistics, mathematics, machine learning, ethics, data management, and a practicum capstone course.

This article describes the motivation for the new ChemE+DS degree, novel aspects of the curriculum, and some of the challenges we experienced during the creation and rollout of the new degree...