Pettis, C., & Swamidurai, R., & Abebe, A., & Shannon, D. (2018, June), Infusion of Big Data Concepts Across the Undergraduate Computer Science Mathematics and Statistics Curriculum  Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. 10.18260/1-2--30662

\bibitem{asee_peer_30662}
  Pettis, C., \& Swamidurai, R., \& Abebe, A., \& Shannon, D. (2018, June), \emph{Infusion of Big Data Concepts Across the Undergraduate Computer Science Mathematics and Statistics Curriculum}
  Paper presented at 2018 ASEE Annual Conference \& Exposition , Salt Lake City, Utah.
  10.18260/1-2--30662

Carl Pettis, Rajendran Swamidurai, Ash Abebe, and David Shannon.  "Infusion of Big Data Concepts Across the Undergraduate Computer Science Mathematics and Statistics Curriculum".  2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah, 2018, June.  ASEE Conferences, 2018.  https://peer.asee.org/30662 Internet.  15 Jan, 2025

\bibitem{asee_peer_30662}
  Carl Pettis, Rajendran Swamidurai, Ash Abebe, and David Shannon.
  "Infusion of Big Data Concepts Across the Undergraduate Computer Science Mathematics and Statistics Curriculum".
  \emph{2018 ASEE Annual Conference \& Exposition , Salt Lake City, Utah, 2018, June}.
  ASEE Conferences, 2018.
  https://peer.asee.org/30662 Internet.  15 Jan, 2025

@INPROCEEDINGS{asee_peer_30662
author = "Carl Pettis, Rajendran Swamidurai, Ash Abebe, and David Shannon"
title = "Infusion of Big Data Concepts Across the Undergraduate Computer Science Mathematics and Statistics Curriculum"
booktitle = "2018 ASEE Annual Conference \& Exposition "
year = "2018"
month = "June"
address = "Salt Lake City, Utah"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/30662}
number = {10.18260/1-2--30662}
}

TY  - CPAPER
AB  - This paper presents our three years’ experience in adapting and integrating big data concepts across the computer science undergraduate mathematics and statistics curriculum. Undergraduate computer science degree courses in mathematics and statistics teaches students the traditional logical and problem-solving skills; but that is only a small part of what is required of graduates when they enter the data science or big data analytics work force. Industry demands a much broader perspective: that of being equipped with technical skills in useful information retrieval from very large, complex, and unstructured data. Universities are waking up to the need for having dedicated big data analytics programs. But, most of the courses are on the graduate level. However, after careful analysis, it is identified that there is a lag in these courses providing channelized big data concepts training to the students, particularly to undergraduate computer science students. This paper serves to address this gap by providing an experience in infusing, teaching, and assessing big data modules in computer science undergraduate mathematics and statistics courses.
AU  - Carl Pettis
AU  - Rajendran Swamidurai
AU  - Ash Abebe
AU  - David Shannon
CY  - Salt Lake City, Utah
DA  - 2018/06/23
PB  - ASEE Conferences
TI  - Infusion of Big Data Concepts Across the Undergraduate Computer Science Mathematics and Statistics Curriculum
UR  - https://peer.asee.org/30662
DO  - 10.18260/1-2--30662
ER  -