Computing and data science skills are without doubt extremely valuable for modern (chemical) engineers. Big data, machine learning, predictive modeling, decision science and similar terms are ever-present in job posting, scientific literature, funding announcements, and popular news. Yet, many chemical engineers lack a background in the fundamentals of computer programming, applied statistics, and mathematical modeling for problem solving. Often, student excitement in data-centric topics manifest through self-study with tutorials, extracurricular projects, and online classes whereby students assemble a toolbox of skills but do not learn the fundamentals that transcend each technique.

In this contribution, I will discuss our ongoing efforts at the University of Notre Dame to create a coherent, integrated strategy for computing and data analysis in the undergraduate curriculum. A key focus is retooling the sophomore-level “Numerical and Statistical Analysis” course (required) to provide a scaffolding for all students to develop core competencies in computing, applied statistics, and mathematical modeling throughout their undergraduate experience and profession careers. Beginning in Spring 2019, we are transitioning from MATLAB to Python for several reasons including consistency with “Chemical Process Control” (junior, required) and college-wide electives in data science and statistical computing that already use Python. I will also share experiences using Jupyter notebooks and cloud-based computing platforms such as Colaboratory to incorporate active learning into lectures and tutorials and to remove technical barriers for students. Content and assignments have been reorganized to emphasize mastery of foundational skills in preference over content breadth. For example, students are now required to submit hand-written pseudocode for all assignments to reinforce essential programming habits. We now emphasize statistical reasoning over more procedural approaches to data analysis, which required adding lectures on probability theory, a new topic for most students. By developing fundamental skills, we seek to better position students to continue learning about numerical methods, data analysis, or scientific computing, either through technical electives at Notre Dame or other means as practicing professionals. Ultimately, we envision “Numerical and Statistical Analysis” as the entry point to computing, with techniques used for problem solving throughout the undergraduate curriculum including statistical analysis in lab, nonlinear equation solving in thermodynamics and separations, numeric integration in reactions and transport, and optimization in controls and design to name a few.