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Data Mining Course in the Undergraduate Computer Science Curriculum

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Conference

2019 ASEE Annual Conference & Exposition

Location

Tampa, Florida

Publication Date

June 15, 2019

Start Date

June 15, 2019

End Date

June 19, 2019

Conference Session

Curriculum and Assessment I

Tagged Division

Computing and Information Technology

Tagged Topic

Diversity

Page Count

18

DOI

10.18260/1-2--32576

Permanent URL

https://peer.asee.org/32576

Download Count

299

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Paper Authors

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Reza Sanati-Mehrizy Utah Valley University

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Reza Sanati-Mehrizy is a professor of Computer Science Department at Utah Valley University, Orem, Utah. He received his M.S. and Ph.D. in Computer Science from the University of Oklahoma, Norman, Oklahoma. His research focuses on diverse areas such as: Database Design, Data Structures, Artificial Intelligence, Robotics, Computer Aided Manufacturing, Data Mining, Data Warehousing, and Machine Learning.

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Kailee Parkinson Utah Valley University

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I am a student at Utah Valley University majoring in Computer Science. I am currently an Engineering Writing Fellow and have written previously on implementing Data Mining courses at an undergraduate level. I am the recipient of the Student Excellence Award in Computer Science in the UVU College of Technology & Computing.

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Elham Vaziripour Utah Valley University

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Elham Vaziripour, Ph.D. in computer science, is currently a professor assistant at Utah Valley University. Her area of research is Security, UX research, and Data analysis. She graduated recently, Dec 2018, from Brigham Young University. Her dissertation was on analyzing security and privacy of secure messaging applications.

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Afsaneh Minaie Utah Valley University

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Afsaneh Minaie is a Professor and Chair of Engineering Department at Utah Valley University. She received her B.S., M.S., and Ph.D. all in Electrical Engineering from University of Oklahoma. Her research interests include gender issues in the academic sciences and engineering fields, Embedded Systems Design, Mobile Computing, Wireless Sensor Networks, Nanotechnology, Data Mining and Databases.

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Abstract

Abstract Data mining combines tools from statistics, neural networks, and machine learning with database management to analyze large data sets. It is a well-researched area of computer science with high demand due to its usefulness in any field with large quantities of data, where meaningful patterns and rules can be extracted. Therefore, many organizations and businesses can benefit from data mining techniques, as these organizations record a massive amount of data daily. The field of Data Mining is growing rapidly and there is increasing interest in providing students with a foundation in this area. It is crucial that the emerging field of Data Mining be integrated into the Computer Science curricula. This paper will study different approaches that are used by different institutions of higher education to integrate Data Mining concepts into their curriculum. And finally makes recommendation that how it should be taught in undergraduate computer science program and what to include in that course(s) in general and specifically in our institution.

Sanati-Mehrizy, R., & Parkinson, K., & Vaziripour, E., & Minaie, A. (2019, June), Data Mining Course in the Undergraduate Computer Science Curriculum Paper presented at 2019 ASEE Annual Conference & Exposition , Tampa, Florida. 10.18260/1-2--32576

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