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Identifying the Best Admission Criteria for Data Science Using Machine Learning

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2018 ASEE Annual Conference & Exposition


Salt Lake City, Utah

Publication Date

June 23, 2018

Start Date

June 23, 2018

End Date

July 27, 2018

Conference Session

Graduate Recruitment and Retention

Tagged Division

Graduate Studies

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Anahita Zarei University of the Pacific

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Dr. Anahita Zarei earned her PhD in Electrical Engineering from University of Washington, Seattle in 2007 and subsequently took up a faculty position at department of Computer and Electrical Engineering at University of the Pacific. In 2014, she joined the Data Science program where she has been teaching courses in Statistical Learning, Machine Learning, and Research Methods. Her research interests include signal processing and application of computational intelligence.

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Rick Hutley University of the Pacific

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Big data is taking the world by storm. What we can do today with the abundant of data and the available technology is extraordinary. Big data analytics has become a game changer in many industries: Healthcare analytics has the potential to reduce cost and improve the quality of patientcare; Insurance companies use data analytics for risk assessment and fraud detection; Legal analytics has made it possible for law professionals to gain deep insight from past litigations to develop better informed strategies and improve efficiency. Number of industries employing big data analytics to improve business decisions is countless. Due to utilization of analytics by large array of industries, this field has attracted many people from diverse academic backgrounds to purse a degree in Analytics and Data Science.

Considering this tremendous demand for data scientists, our institution launched a Masters degree in Data Science in 2014. This is a two-year program covering courses in rigorous Math and programming, as well as courses entailing soft skills such as visual storytelling and consulting skills.

One of the challenges that has faced faculty on the admission committee in the past few years is selecting the right criteria for student admission. Typically, in engineering disciplines the admission decision is based on students’ performance on courses such as calculus, physics and pre-engineering topics. However, due to the nature of Data Science field the applicants come from very diverse undergraduate programs. For instance, some of our top graduating students had an undergraduate degree in Creative Writing or Healthcare. We have witnessed many cases in which the admission criteria that are commonly used in other technical fields did not necessarily translate to identification of successful candidates for the Data Science program. Finding the right students who will be successful in this program is crucial both for the candidates and the university to save resources.

The objective of this study is to identify a set of rules based on previous admission decisions and achievement of admitted students to capture the characteristics of a successful admission. We apply statistical and machine learning techniques (using 4 cohorts’ information for training and 1 cohort for test) to provide us with a better set of guidelines for future admission processes.

Zarei, A., & Hutley, R. (2018, June), Identifying the Best Admission Criteria for Data Science Using Machine Learning Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--29638

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