(241b) Chemical Engineering Students’ Perception of and Attitudes Towards Data Science

The future of chemical engineering (CHE) is changing relative to the impact of novel analytics and large-scale computations. However, CHE curricula have not kept pace with the increasingly complex problems that accompany ongoing technological advancements. Hence, there is greater importance in developing skills in data science as the use of technology continues to escalate. Understanding the attitudes that CHE students have towards Python, Excel/VBA, statistics, Matlab, and other data science instruments will allow universities to improve their method of administering skills and knowledge that are vital to overcoming challenges faced in the industry.

This research utilized a survey to determine how and what specific factors influence the perceptions 100 chemical engineering students at the University of Illinois at Chicago (UIC) have surrounding this matter. In addition to the computing teacher’s demographics, the analysis conducted also investigated how the students’ demographic characteristics, skills in computing, class standing, and industry and research experiences play a role in their viewpoints.

The research team collected both quantitative and qualitative feedback from the participants’ survey evaluations. With the use of a Likert scale of five or ten, numerical responses were gathered from questions geared towards the participants determining the degree to which a certain statement pertains to them. In addition, participants were allowed to further explain the reasoning for their stance on a particular topic. Verbal responses from these entries enabled the research team to have further insight into how specific variables have a greater influence on one group of participants than another. From the data collected, the research team viewed and cross-referenced the trends of the responses to draw conclusions on whether, and how, the criteria previously mentioned play a role in students’ viewpoints towards learning data science.

This paper will provide a detailed explanation of how the survey was developed and analyzed, data distribution and findings, and methods in which universities can better develop and integrate future data science programs into their curricula.