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Infusion of Big Data Concepts Across the Undergraduate Computer Science Mathematics and Statistics Curriculum

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Conference

2018 ASEE Annual Conference & Exposition

Location

Salt Lake City, Utah

Publication Date

June 23, 2018

Start Date

June 23, 2018

End Date

July 27, 2018

Conference Session

Mathematics Division Technical Session 3

Tagged Division

Mathematics

Page Count

10

Permanent URL

https://peer.asee.org/30662

Download Count

23

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Paper Authors

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Carl Pettis Alabama State University

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Dr. Carl S. Pettis is a Professor of Mathematics at Alabama State University. He received his BS degree in 2001 and his MS degree in 2003 both from Alabama State University in Mathematics. Dr. Pettis received his PhD in Mathematics from Auburn University in 2006. He currently serves as the Interim Associate Provost for the Office of Academic Affairs.

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Rajendran Swamidurai Alabama State University

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Dr. Rajendran Swamidurai is an Associate Professor of Computer Science at Alabama State University. He received his BE in 1992 and ME in 1998 from the University of Madras, and PhD in Computer Science and Software Engineering from Auburn University in 2009. He is an IEEE senior Member.

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Ash Abebe Auburn University

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Ash Abebe is a professor of statistics at Auburn University. He received a B.Sc. in statistics from Addis Ababa University, Ethiopia, in 1995 and a Ph.D. in statistics from Western Michigan University in 2002. His research focus is on developing non-parametric statistical methods for analyzing complex data, especially those derived from spatio-temporal processes.

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David Shannon Auburn University

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Dr. Shannon has a Ph.D. in Educational Research and Evaluation Methodology and Statistics from the University of Virginia and is currently the Humana-Sherman-Germany Distinguished Professor at AU. He teachers courses in research methods and program evaluation.

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Abstract

This paper presents our three years’ experience in adapting and integrating big data concepts across the computer science undergraduate mathematics and statistics curriculum. Undergraduate computer science degree courses in mathematics and statistics teaches students the traditional logical and problem-solving skills; but that is only a small part of what is required of graduates when they enter the data science or big data analytics work force. Industry demands a much broader perspective: that of being equipped with technical skills in useful information retrieval from very large, complex, and unstructured data. Universities are waking up to the need for having dedicated big data analytics programs. But, most of the courses are on the graduate level. However, after careful analysis, it is identified that there is a lag in these courses providing channelized big data concepts training to the students, particularly to undergraduate computer science students. This paper serves to address this gap by providing an experience in infusing, teaching, and assessing big data modules in computer science undergraduate mathematics and statistics courses.

Pettis, C., & Swamidurai, R., & Abebe, A., & Shannon, D. (2018, June), Infusion of Big Data Concepts Across the Undergraduate Computer Science Mathematics and Statistics Curriculum Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. https://peer.asee.org/30662

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