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DataRobot In the Classroom
Technology Category
- Analytics & Modeling - Machine Learning
Applicable Industries
- Education
Applicable Functions
- Product Research & Development
Use Cases
- Predictive Maintenance
Services
- Training
The Challenge
Smith School of Business at Queen’s University in Canada is known for its innovative approach to business education, including creating ground-breaking programs and courses in emerging areas including artificial intelligence, fintech, analytics, cultural diversity, team dynamics, social impact and more. Anton Ovchinnikov, Distinguished Professor of Management Analytics at the Smith School of Business, teaches courses in predictive modeling, data science and machine learning. His students are typically working professionals who are consumers of analytics, not producers. Many of them are, or will soon be, managers of analytical projects and teams. As part of Anton’s courses, he wants his students to familiarize themselves with the raw coding, at least at a basic level, in order to fully understand what’s behind the curtain of what they’re trying to predict. However, the manual coding process can be time-consuming and complex, leading to a need for a more efficient solution.
About The Customer
The customer in this case study is the Smith School of Business at Queen’s University in Canada. Established in 1919 and located in Kingston, Ontario, the school has received international recognition for its innovative approach to business education. This includes creating ground-breaking programs and courses in emerging areas such as artificial intelligence, fintech, analytics, cultural diversity, team dynamics, social impact and more. The students at the school are typically working professionals who are consumers of analytics, not producers. Many of them are, or will soon be, managers of analytical projects and teams.
The Solution
Anton introduced his students to DataRobot’s automated machine learning platform. The platform was used in the context of a case study on Retention Modeling at a Scholastic Travel Company, which is a retention modeling exercise focused on predicting customer churn. After a week of working on the case assignment by manually coding predictive models, the students were introduced to how DataRobot and automation can be a solution. The students then had access to the DataRobot platform to play around with for a limited time. According to Anton, a number of them found automated machine learning to be so valuable that they began commercial evaluations at their companies. This solution not only provided the students with a practical understanding of automated AI applications but also equipped them with a valuable tool for their future careers.
Operational Impact
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