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Applications of Linear Algebra Applied to Big Data Analytics

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Conference

2020 ASEE Virtual Annual Conference Content Access

Location

Virtual On line

Publication Date

June 22, 2020

Start Date

June 22, 2020

End Date

June 26, 2021

Conference Session

Mathematics Division Technical Session 5: From Functions to Big Data–A Hands-on Challenge

Tagged Division

Mathematics

Tagged Topic

Diversity

Page Count

11

DOI

10.18260/1-2--34153

Permanent URL

https://peer.asee.org/34153

Download Count

189

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Paper Authors

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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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Cadavious M. Jones

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Dr. Cadavious M. Jones is an Associate Professor of Mathematics at Alabama State University. He received his BS in 2006 and MS in 2008 from Alabama State University, and PhD in Mathematics from Auburn University in 2014. He is a contributor to the Australian Maths Trust, and member of the MASAMU international research group for mathematics.

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Carl Pettis

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Carl S. Pettis, Ph.D.
Professor of Mathematics
Department of Mathematics and Computer Science
Alabama State University

Administrative role:

Interim Associate Provost
Office of Academic Affairs
Alabama State University

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Uma Kannan

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Dr. Uma Kannan is Assistant Professor of Computer Information Systems in the College of Business Administration at Alabama State University, where she has taught since 2017. She received her Ph.D. degree in Cybersecurity from Auburn University in 2017. She specialized in Cybersecurity, particularly on the prediction and modelling of insidious cyber-attack patterns on host network layers. She also actively involved in core computing courses teaching and project development since 1992 in universities and companies.

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Abstract

The field of Big Data Analytics continues to rapidly grow and encompass a greater number of techniques from a host of fields. Through the utilization of activities and reading assignments aimed to stimulate students’ interest in the field of Big Data Analytics we make them aware that Linear Algebra plays an important role in Big Data, given certain inherent constraints found in the Linear Algebra course curriculum. Key concepts in the undergraduate curriculum are stressed as to their role in Big Data. We start with techniques that enhance basic principles of Linear Algebra, such as understanding the algorithm for matrix multiplication and transition matrices, and how they relate to data and provide context, then through modules focusing on relevant real world scenarios we present appropriate analysis methods. In particular, we examine the Leslie Matrix and population change, as well as the PageRank (PR) algorithm used by Google amongst other applicable Linear Algebra techniques. Based on student outcomes we consider challenge while aiming to seamlessly integrate Big Data Analytics with our Linear Algebra course.

Swamidurai, R., & Jones, C. M., & Pettis, C., & Kannan, U. (2020, June), Applications of Linear Algebra Applied to Big Data Analytics Paper presented at 2020 ASEE Virtual Annual Conference Content Access, Virtual On line . 10.18260/1-2--34153

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