*Result*: Business Analytics Competition (BAC@MC): A learning experience.

Title:
Business Analytics Competition (BAC@MC): A learning experience.
Authors:
Ammar, Salwa1 (AUTHOR), Kim, Min Jung2 (AUTHOR), Masoumi, Amir H.3 (AUTHOR), Tomoiaga, Alin1 (AUTHOR) alin.tomoiaga@manhattan.edu
Source:
Decision Sciences Journal of Innovative Education. Apr2023, Vol. 21 Issue 2, p52-67. 16p. 1 Diagram, 4 Charts, 4 Graphs, 3 Maps.
Geographic Terms:
Company/Entity:
Database:
Business Source Premier

*Further Information*

*Over the past few years, academics have undertaken initiatives to bridge the gap between theory and practice in the ever‐growing field of business analytics, including implementing real‐life student projects in all shapes and forms. Every year since 2015, Manhattan College has invited student teams from across North America and elsewhere in the world to its campus in order to participate in an intercollegiate business analytics competition (BAC@MC). This well‐received event and the objectives behind it are described in this article. The program is shown to serve as an effective experiential learning adventure for the undergraduate students as it hones their data analytic skills in the context of an engaging real‐world business problem. The roles various stakeholders play in this high‐impact practice are highlighted. Furthermore, an example of a recent competition question is presented (along with a summary of the analytical approaches attempted) by the student teams. Descriptive visualizations, regression, and cluster algorithms implemented using python, R, Excel, or Tableau are among the typical analyses utilized by participating students. As witnessed by the students, faculty advisors, and the industry practitioners who attended the event, competitions such as BAC@MC can be rewarding, community‐building, and transformative experiences for undergraduate students who will soon become tomorrow's business analysts. [ABSTRACT FROM AUTHOR]

Copyright of Decision Sciences Journal of Innovative Education is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)*

*Full text is not displayed to guests*