Does pair programming work in a data science context? An initial case study

Jeffrey S. Saltz, Ivan Shamshurin

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

4 Scopus citations

Abstract

While pair programming has been studied extensively for software programmers, very little has been reported with respect to pair programming in a data science project. This paper reports on a case study evaluating the effectiveness of pair programming within a data science / big data context. Our findings show that pair programming can be useful for data science teams. In addition, while the driver role was similar to what has been described for software programmers, we note that the observer role had an expanded set of responsibilities, which we termed researcher activities. Further exploration is required to explore if these expanded roles are specific to data science pair programming.

Original languageEnglish (US)
Title of host publicationProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
EditorsJian-Yun Nie, Zoran Obradovic, Toyotaro Suzumura, Rumi Ghosh, Raghunath Nambiar, Chonggang Wang, Hui Zang, Ricardo Baeza-Yates, Ricardo Baeza-Yates, Xiaohua Hu, Jeremy Kepner, Alfredo Cuzzocrea, Jian Tang, Masashi Toyoda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2348-2354
Number of pages7
ISBN (Electronic)9781538627143
DOIs
StatePublished - Jul 1 2017
Event5th IEEE International Conference on Big Data, Big Data 2017 - Boston, United States
Duration: Dec 11 2017Dec 14 2017

Publication series

NameProceedings - 2017 IEEE International Conference on Big Data, Big Data 2017
Volume2018-January

Other

Other5th IEEE International Conference on Big Data, Big Data 2017
CountryUnited States
CityBoston
Period12/11/1712/14/17

Keywords

  • Big Data
  • Data Science
  • Pair Programming
  • Process Methodology
  • Project Management

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Hardware and Architecture
  • Information Systems
  • Information Systems and Management
  • Control and Optimization

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