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Advising Student-Driven Analytics Projects: A Summary of Experiences and Lessons Learned

Author

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  • Aaron Babier

    (Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada; Vector Institute, Toronto, Ontario M5G 1M1, Canada)

  • Craig Fernandes

    (Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada)

  • Ian Yihang Zhu

    (Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario M5S 3G8, Canada)

Abstract

In this paper, we describe a course project in which teams of undergraduate students propose and execute an end-to-end analytics project to solve a real-world problem. The project challenges students to implement machine learning, optimization, simulation, or a combination of these three techniques on real-world data that they collect. A designated project advisor helps each team refine its project and assesses the quality of the resulting work. In our analysis of 58 past projects, we show that students developed solutions for a wide range of topics by employing various methodologies. However, most teams encountered similar challenges that project advisors helped them overcome with tailored feedback. Based on feedback from 106 previous students, the project experience was largely positive and helped them prepare for their future careers. We believe that this type of hands-on project is conducive to the development of important data analytics skills.

Suggested Citation

  • Aaron Babier & Craig Fernandes & Ian Yihang Zhu, 2023. "Advising Student-Driven Analytics Projects: A Summary of Experiences and Lessons Learned," INFORMS Transactions on Education, INFORMS, vol. 23(2), pages 121-135, January.
    • Handle: RePEc:inm:orited:v:23:y:2023:i:2:p:121-135
    DOI: 10.1287/ited.2022.0275
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    References listed on IDEAS

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