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Sat, Nov 7, 8:30 - 8:55, Crystal 5     Paper (refereed)
Recommended Citation: Jafar, M J and R Anderson.  A Tools-based Approach to Teaching Data Mining Methods.  In The Proceedings of the Information Systems Education Conference 2009, v 26 (Washington DC): §3153. ISSN: 1542-7382.
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A Tools-based Approach to Teaching Data Mining Methods

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Refereed24 pages
Musa J. Jafar    [a1] [a2]
CIS Department
West Texas A&M University    [u1] [u2]
Canyon, Texas, USA    [c1] [c2]

Russell Anderson    [a1] [a2]
West Texas A&M University    [u1] [u2]
Canyon, Texas, USA    [c1] [c2]

In this paper, we describe how we used Microsoft Excel’s data mining add-ins and cloud computing components to teach our senior data mining class. The tools were part of a larger set of tools that we used as part of SQL Server Business Intelligence Development Studio. We also demonstrate the ease of use of these tools to teach a course in data-mining methods with focus on elementary data analysis, data mining algorithms and the usage of the algorithms to analyze data in support of decision-making and business intelligence. The tools allow faculty to focus on the analytical aspects of the algorithms, data mining analysis and practical hands-on homework assignments and projects. The tools allow students to gain conceptual understanding of data mining, hands-on practical experience in data mining algorithms using and analysis of data using data mining tools for the purpose of decision support without having to write large amounts of code to implement the algorithms. We also demonstrate that without such tools, it would have been impossible for a faculty to provide a comprehensive coverage of the topic in a first course in data mining methods. The availability of such tools transform the role of a student from a programmer of data mining algorithms to a business intelligence analyst who understands the algorithms and uses a set of tools that implement these algorithms to analyze data for the purpose of decision support.

Keywords: Data mining, Decision Support, Business Intelligence, Excel Data mining Add-ins, Cloud Computing

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