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

\bibitem{asee_peer_22525}
  King, B. R., \& Satyanarayana, A. (2013, June), \emph{Teaching Data Mining in the Era of Big Data}
  Paper presented at 2013 ASEE Annual Conference \& Exposition, Atlanta, Georgia.
  10.18260/1-2--22525

Brian R. King and Ashwin Satyanarayana.  "Teaching Data Mining in the Era of Big Data".  2013 ASEE Annual Conference & Exposition, Atlanta, Georgia, 2013, June.  ASEE Conferences, 2013.  https://peer.asee.org/22525 Internet.  15 Dec, 2024

\bibitem{asee_peer_22525}
  Brian R. King and Ashwin Satyanarayana.
  "Teaching Data Mining in the Era of Big Data".
  \emph{2013 ASEE Annual Conference \& Exposition, Atlanta, Georgia, 2013, June}.
  ASEE Conferences, 2013.
  https://peer.asee.org/22525 Internet.  15 Dec, 2024

@INPROCEEDINGS{asee_peer_22525
author = "Brian R. King and Ashwin Satyanarayana"
title = "Teaching Data Mining in the Era of Big Data"
booktitle = "2013 ASEE Annual Conference \& Exposition"
year = "2013"
month = "June"
address = "Atlanta, Georgia"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/22525}
number = {10.18260/1-2--22525}
}

TY  - CPAPER
AB  -                       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.
AU  - Brian R. King
AU  - Ashwin Satyanarayana
CY  - Atlanta, Georgia
DA  - 2013/06/23
PB  - ASEE Conferences
TI  - Teaching Data Mining in the Era of Big Data
UR  - https://peer.asee.org/22525
DO  - 10.18260/1-2--22525
ER  -