Journal of Information Systems Applied Research

Volume 9

V9 N2 Pages 4-12

October 2016

A Comparison of Open Source Tools for Data Science

Hayden Wimmer
Georgia Southern University
Atlanta, GA 30302 , USA

Loreen Marie Powell
Bloomsburg University
Bloomsburg, PA 17815, USA

Abstract: The next decade of competitive advantage revolves around the ability to make predictions and discover patterns in data. Data science is at the center of this revolution. Data science has been termed the sexiest job of the 21st century. Data science combines data mining, machine learning, and statistical methodologies to extract knowledge and leverage predictions from data. Given the need for data science in organizations, many small or medium organizations are not adequately funded to acquire expensive data science tools. Open source tools may provide the solution to this issue. While studies comparing open source tools for data mining or business intelligence exist, an update on the current state of the art is necessary. This work explores and compares common open source data science tools. Implications include an overview of the state of the art and knowledge for practitioners and academics to select an open source data science tool that suits the requirements of specific data science projects.

Keywords: Business Intelligence, Data Mining, Data Science Tools, open source, Predictive Analytics

Download this article: JISAR - V9 N2 Page 4.pdf

Recommended Citation: Wimmer, H., Powell, L. M. (2016). A Comparison of Open Source Tools for Data Science. Journal of Information Systems Applied Research, 9(2) pp 4-12. http://jisar.org/2016-9/ ISSN: 1946-1836. (A preliminary version appears in The Proceedings of CONISAR 2015)