Information Systems Education Journal

Volume 16

V16 N1 Pages 33-40

February 2018

A Comparison of Key Concepts in Data Analytics and Data Science

Kirby McMaster
Weber State University
Ogden, UT 84480, USA

Stuart L. Wolthuis
Brigham Young University-Hawaii
Laie, HI 96762, USA

Brian Rague
Weber State University
Ogden, UT 84480, USA

Samuel Sambasivam
Azusa Pacific University
Azusa, CA 91702, USA

Abstract: This research study provides an examination of the relatively new fields of Data Analytics and Data Science. We compare word rates in Data Analytics and Data Science documents to determine which concepts are mentioned most often. The most frequent concept in both fields is data. The word rate for data is more than twice the next highest word rate, which is for model. This contrasts sharply with how often the word data appears in most Mathematics books. Overall, we observed substantial agreement on important concepts in Data Analysis and Data Science. Eighteen of the 25 most frequent concepts are shared by both fields. One difference is that the words problem and solution had Top 25 word rates for Data Science, but not for Data Analytics. A close look at Statistics concepts suggests that Data Analytics is more focused on exploratory concerns, such as searching for patterns in data. Data Science retains more of the classical inferential activities that use sample data to draw conclusions about populations. Both fields deal with Big Data situations, but Data Scientists must continue to be prepared for traditional small sample applications.

Keywords: Data, data analytics, data science, exploratory, inference, statistics

Download this article: ISEDJ - V16 N1 Page 33.pdf

Recommended Citation: McMaster, K., Wolthuis, S. L., Rague, B., Sambasivam, S. (2018). A Comparison of Key Concepts in Data Analytics and Data Science . Information Systems Education Journal, 16(1) pp 33-40. ISSN: 1545-679X. (A preliminary version appears in The Proceedings of EDSIG 2017)