Identifying the most Common Frameworks Data Science Teams Use to Structure and Coordinate their Projects

Jeffrey S. Saltz, Nicholas Hotz

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents the results of a study focused on exploring which framework, if any, teams use to execute data science projects. The study consisted of a survey of 109 industry professionals, as well as an evaluation of relevant framework terms searched at Google. Overall, CRISP-DM was the most commonly used framework, with Scrum and Kanban being the second and third most frequently used. We note that CRISP-DM is a life cycle framework, whereas Scrum and Kanban are team coordination frameworks. Hence, this research also notes the potential demand for a framework that integrates both life cycle and team coordination aspects of leading a data science project.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
EditorsXintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2038-2042
Number of pages5
ISBN (Electronic)9781728162515
DOIs
StatePublished - Dec 10 2020
Event8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States
Duration: Dec 10 2020Dec 13 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference8th IEEE International Conference on Big Data, Big Data 2020
Country/TerritoryUnited States
CityVirtual, Atlanta
Period12/10/2012/13/20

Keywords

  • Big Data
  • Data Science
  • Process Methodology

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

Fingerprint

Dive into the research topics of 'Identifying the most Common Frameworks Data Science Teams Use to Structure and Coordinate their Projects'. Together they form a unique fingerprint.

Cite this