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Teaching Data Mining in the Era of Big Data

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2013 ASEE Annual Conference & Exposition


Atlanta, Georgia

Publication Date

June 23, 2013

Start Date

June 23, 2013

End Date

June 26, 2013



Conference Session

Curricular Issues in Computing and Information Technolog Programs

Tagged Division

Computing & Information Technology

Page Count


Page Numbers

23.1140.1 - 23.1140.22



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Paper Authors


Brian R. King Bucknell University

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Brian R. King is an Assistant Professor in computer science at Bucknell University, where he teaches introductory courses in programming, as well as advanced courses in software engineering and data mining. He graduated in 2008 with his PhD in Computer Science from University at Albany, SUNY. Prior to completing his PhD, he worked 11 years as a Senior Software Engineer developing data acquisition systems for a wide range of real-time environmental quality monitors. His research interests are in bioinformatics and data mining, specifically focusing on the development of methods that can aid in the annotation, management and understanding of large-scale biological sequence data.

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Ashwin Satyanarayana New York City College of Technology

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Dr. Satyanarayana serves as an Assistant Professor in the Department of Computer Systems Technology at New York City College of Technology (CUNY). He received both his MS and PhD degrees in Computer Science from SUNY – Albany in 2006 with Specialization in Data Mining. Prior to joining CUNY, he was a Research Scientist at Microsoft, involved in Data Mining Research in Bing for 5 years. His main areas of interest are Data Mining, Machine Learning, Data Preparation, Information Theory, Applied Probability with applications in Real World Learning Problems.
Address: Department of Computer Systems Technology, N-913, 300 Jay Street, Brooklyn, NY-11201.

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Teaching Data Mining in the Era of Big DataThe amount of data being generated and stored is growing exponentially, owed in part to thecontinuing advances in computer technology. These data present tremendous opportunities indata mining, a burgeoning field in computer science that focuses on the development of methodsthat can extract knowledge from data. Recent studies have noted the rise of data mining as acareer path with increasing opportunities for graduates. These opportunities are not onlyavailable in the private sector; the U.S. government has recently invested $200 million in “bigdata” research. These suggest the importance for us to teach the tools and techniques that areused in this field.Data mining introduces new challenges for faculty in universities who teach courses in this area.Some of these challenges include: providing access to large real world data for students,selection of tools and languages used to learn data mining tasks, and reducing the vast pool oftopics in data mining to those that are critical for success in a one-semester undergraduatecourse.In this paper, we address the above issues by providing faculty with important criteria that webelieve have high potential for use in an undergraduate course in data mining. We first discuss aset of core topics that such a course should include. A set of practical, widely-accepted tools andlanguages used for data mining are summarized. We provide a list of real datasets that can beuseful for possible course assignments and projects. Our paper is based on our collectiveresearch and industry experience in data mining, and on the development of an undergraduatecourse in data mining that was taught for the first time in 2011. We conclude by providing somechallenges to motivate students to pursue future work in the area of data mining.

King, B. R., & Satyanarayana, A. (2013, June), Teaching Data Mining in the Era of Big Data Paper presented at 2013 ASEE Annual Conference & Exposition, Atlanta, Georgia. 10.18260/1-2--22525

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