Treffer: Introducing Data Analytics into Mechanical Engineering Curriculum.

The advancements in information technology, computing power, data mining and artificial intelligence have enabled all the engineering disciplines to take the advantages of large datasets to model, classify, and make proper predictions for numerous engineering applications. To educate nextgeneration mechanical engineers in the new era of data science and artificial intelligence, engineering educators have been urged to integrate these new technological advancements into existing curriculum to adapt to the fast-changing needs from the future workplace. My effort primarily focuses on implementing an interdisciplinary approach to introduce the concepts and principles of data science to the undergraduate students of mechanical engineering. I re-designed the class of Statistics for Engineering as Data Analytics for Engineering, in which the students can practice new tools used in data analytics applications while they are still learning the basic statistical principles behind these techniques. In this class, the students are exposed to the real-world examples of how data analytics has been applied in the field of mechanical engineering. The course content arrangement is based on the data analytics lifecycle: problem discovery - data understanding - data preparation - data visualization - model building - conclusion/decision making. Statistical concepts related to each stage are introduced to the students along with the corresponding programming basics in R-studio. Parallelly, the semesterlong project is assigned to the student groups from the first day of lecture. Each group is required to select a real-world dataset and complete the data analysis using data cleaning, data preparation, data visualization, regressions, and several machine learning algorithms. To help the student better complete the project, I develop interactive activities at different development stages, including project proposal, proposal peer review, project interview, preliminary report, and final report. The assessment of the effectiveness of this new class was conducted by comparing exams and feedback of students by the end of the semester. This class provides students with sufficient knowledge of both fundamental statistics and practical data analytical techniques for engineering fields, comprehensive experience in data analytic workflow, and the opportunity to exercise their data analytical skills in engineering applications. [ABSTRACT FROM AUTHOR]

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