Crowning another successful MScA quarter, the Summer 2019 Capstone Showcase took place on Saturday, August 24 with teams making strong cases for creative solutions while using entrepreneurial-minded approaches to derive their innovative insights.
Albert Chan, MScA Associate Director, expressed his congratulations to the presenters and the advisors on work well done, adding that he was “very pleased with the success of the event. Let’s acknowledge the work and dedication of the students, as well as their capstone supervisors, for putting their best efforts forward,” he said.
He also noted his appreciation and honor to have a jury comprised of select MScA instructors, industry partners, and alumna that included Shaddy Abado, MScA instructor and Senior Data Scientist at Ulta, Francisco Azeredo, MScA instructor and Senior Vice President, Specialist, Enterprise Analytics & Strategy at Northern Trust, Emily Coppess, MScA Alumna and Associate Data Scientist at Zurich North America, and Teddy Petrova, MScA Alumna and Global Manager, Data Science and Data Engineering Learning at McKinsey & Company.
Receiving the best-in-show award from the jury were Elijah Eaton, Jamie Olds, and Sammazo Plamin for their project “Managerial Feedback: The Elements of Sentiment, Conversation, and Language Associated with Performance Improvement.” Advised by Nick Kadochnikov, they will receive the $5,000 best-in-show prize, which is to be used within a year toward attending an analytics-related conference.
Specifying the insights generated by the team as well as the lucid narrative with which they told it, the jury awarded the prize based on the team’s clear problem statement and the methods they applied to methodically build their solution. The jury also noted their acknowledgment of the limitations in the data and models.
Nominated for an honorable mention were Christine Cho and Arkaparna Mandal whose project “Urban Trip Planner: Designing a Hybrid Recommendation Engine,” advised by Utku Pamuksuz, was recognized for the team’s entrepreneurial approach to developing a recommendation engine.
“They demonstrated an understanding of the text inputs and boundaries,” the jury wrote, “while confidently communicating the methodological foundation for their solution.”