A cross-listed upper division and graduate elective course for students in science, technology, engineering, and mathematics (STEM) fields has been developed to build computational skills in mathematical modeling. The course aims to fills a gap in the practical training of students starting computational research projects across various STEM disciplines who have inconsistent previous experiences in computer programming and numerical methods. This is achieved by covering modern software tools for mathematical modeling in science and engineering and for reproducible research computing via an active, hands-on approach supplemented by reading materials. Rather than covering just the basics of programming or detailed algorithms for numerical methods, the course is geared towards implementing tools for solving realistic continuum scale science and engineering problems, managing open source code projects, and disseminating computational research results through scientific documentation and publications. The course is taught by a chemical engineering faculty member with research expertise in applied mathematics and computational science and engineering. MATLAB and Python are taught side-by-side throughout the course. The paper describes the course with the goal of enabling other educators to adapt and reuse the course content.
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