Abstract:

The increasing reliance on computational methods in higher-level cycles of Engineering curricula necessitates a strategic pedagogical shift in introductory courses. In most contemporary Engineering programs, Python has emerged as the standard language for technical development; however, students often face a steep learning curve when introduced to complex programming tasks in their senior years without prior exposure. This paper explores integrating Python-based workflows into the General Chemistry syllabus for first-year Engineering students. The primary objective is to utilize Python not merely as a programming language, but as a high-level scientific calculator and data management tool that simplifies the transition toward more demanding engineering challenges in the future.

The proposed methodology centers on Jupyter Notebooks as the primary educational environment. This format enables students to develop a "computational narrative" where chemical theory, LaTeX-formatted mathematical equations, and executable code coexist in a single, well-documented document. The curriculum emphasizes the application of specialized libraries such as Mendeleev for elemental data, Pint for rigorous unit conversion, and Matplotlib for the graphical representation of experimental results. Furthermore, all course materials and student projects are managed through GitHub repositories, introducing students to industry-standard version control and collaborative workflows from the onset of their degree.

The paper discusses an evaluation framework to measure how this early exposure correlates with increased student autonomy and technical proficiency. Preliminary observations suggest that shifting the focus from syntax-heavy coding to practical problem-solving in a familiar chemical context significantly reduces the cognitive load associated with later programming requirements. In conclusion, embedding Python and Jupyter Notebooks into early-stage Engineering science courses serves as a critical bridge, ensuring that students possess the necessary digital literacy to succeed in the data-driven environments of modern Engineering.

Keywords:

Scientific Computing, Jupyter Notebooks, Engineering Education, Computational Thinking.