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ERIC Number: EJ872700
Record Type: Journal
Publication Date: 2010
Pages: 24
Abstractor: As Provided
Reference Count: 15
ISBN: N/A
ISSN: ISSN-1547-9714
A Tools-Based Approach to Teaching Data Mining Methods
Jafar, Musa J.
Journal of Information Technology Education, v9 pIIP-2-IIP-24 2010
Data mining is an emerging field of study in Information Systems programs. Although the course content has been streamlined, the underlying technology is still in a state of flux. The purpose of this paper is to describe how we utilized Microsoft Excel's data mining add-ins as a front-end to Microsoft's Cloud Computing and SQL Server 2008 Business Intelligence platforms as back-ends to teach a senior level data mining methods class. The content presented and the hands on experience gained have broader applications in other areas, such as accounting, finance, general business, and marketing. Business students benefit from learning data mining methods and the usage of data mining tools and algorithms to analyze data for the purpose of decision support in their areas of specialization. Our intention is to highlight these newly introduced capabilities to faculty currently teaching a business intelligence course. Faculty interested in expanding their teaching portfolio to the data mining and the business intelligence areas may also benefit from this article. This set of integrated tools allowed us to focus on teaching the analytical aspects of data mining and the usage of algorithms through practical hands-on demonstrations, homework assignments, and projects. As a result, students gained a conceptual understanding of data mining and the application of data mining algorithms for the purpose of decision support. Without such a set of integrated tools, it would have been prohibitive for faculty to provide comprehensive coverage of the topic with practical hands-on experience. The availability of this set of tools transformed the role of a student from a programmer of data mining algorithms to a business intelligence analyst. Students now understand the algorithms and use tools to perform (1) elementary data analysis, (2) configure and use data mining computing engines to build, test, compare and evaluate various mining models, and (3) use the mining models to analyze data and predict outcomes for the purpose of decision support. If it was not for the underlying technologies that we used, it would have been impossible to cover such material in a one-semester course and provide students with much needed hands-on experience in data mining. Finally, what we presented is how to utilize the cloud as a computing platform that transformed the role of a student from "doing low-level IT" in a data mining course to a business intelligence analyst using tools to analyze data for the purpose of decision support. (Contains 22 figures.)
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Publication Type: Journal Articles; Reports - Descriptive
Education Level: Higher Education
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A