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How A Data Mining Course Should Be Taught In An Undergraduate Computer Science Curriculum

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Conference

2010 Annual Conference & Exposition

Location

Louisville, Kentucky

Publication Date

June 20, 2010

Start Date

June 20, 2010

End Date

June 23, 2010

ISSN

2153-5965

Conference Session

POTPOURRI

Tagged Division

Information Systems

Page Count

7

Page Numbers

15.646.1 - 15.646.7

Permanent URL

https://peer.asee.org/16476

Download Count

610

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Paper Authors

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Reza Sanati-Mehrizy Utah Valley University

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Paymon Sanati-Mehrizy University of Pennsylvania

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Afsaneh Minaie Utah Valley University

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Chad Dean Utah Valley University

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Abstract
NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract

How a Data Mining Course Should be Taught in an Undergraduate Computer Science Curriculum

Abstract

Data mining is a relatively new area of computer science that brings the concept of artificial intelligence, data structures, statistics, and database together. It is a high demand area because many organizations and businesses can benefit from it. There is no doubt that it is a great idea to teach a data mining course in computer science curriculum. As you can tell, students taking a data mining course need to have background in quite a few areas to be successful. Not every student taking this course may have the background required in all these areas. The question is how can an instructor remedy the challenge of teaching a group of students with widely-ranging backgrounds, and at what level should this course be taught. Furthermore, the issue of group work arises, specifically as to whether data mining course projects should be accomplished individually or as teams.

Studies show that many universities are teaching data mining course(s) within their computers science curriculum. Each school teaches this course according to their individual methodology. This paper will study different teaching approaches used by different institutions around the country and makes some appropriate teaching recommendations based on the conclusions reached.

Introduction

To research how data mining courses are being taught at the undergraduate level, we searched the websites of computer science departments of various universities around the United States looking for data mining course syllabus, schedule and related supporting materials. The syllabus, schedule and material were then analyzed to discern the basic structure and focus of the class. Not all schools that offer courses in data mining offer them at the undergraduate level. Graduate level courses were not considered for this study but it is interesting to note that graduate level courses follow the same basic patterns discovered in the undergraduate courses.

In this study, we discovered four different teaching models used by different universities to teaching data mining at the undergraduate level. These four different models are: Mathematical/Algorithm Based, Textbook Based, Topical Based, and Applied Data Mining. Of the nine schools we researched, five followed the same pattern. The remaining four schools are divided among three different approaches as showed in figure 1.

Sanati-Mehrizy, R., & Sanati-Mehrizy, P., & Minaie, A., & Dean, C. (2010, June), How A Data Mining Course Should Be Taught In An Undergraduate Computer Science Curriculum Paper presented at 2010 Annual Conference & Exposition, Louisville, Kentucky. https://peer.asee.org/16476

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