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Consultancy Style Dissertations in Statistics and Data Science: Why and How

Author

Listed:
  • Serveh Sharifi Far
  • Vanda Inácio
  • Daniel Paulin
  • Miguel de Carvalho
  • Nicole H. Augustin
  • Mike Allerhand
  • Gail Robertson

Abstract

In this article, we chronicle the development of the consultancy style dissertations of the MSc program in Statistics with Data Science at the University of Edinburgh. These dissertations are based on real-world data problems, in joint supervision with industrial and academic partners, and aim to get all students in the cohort together to develop consultancy skills and best practices, and also to promote their statistical leadership. Aligning with recently published research on statistical education suggesting the need for a greater focus on statistical consultancy skills, we summarize our experience in organizing and supervising such consultancy style dissertations, describe the logistics of implementing them, and review the students’ and supervisors’ feedback about these dissertations.

Suggested Citation

  • Serveh Sharifi Far & Vanda Inácio & Daniel Paulin & Miguel de Carvalho & Nicole H. Augustin & Mike Allerhand & Gail Robertson, 2023. "Consultancy Style Dissertations in Statistics and Data Science: Why and How," The American Statistician, Taylor & Francis Journals, vol. 77(3), pages 331-339, July.
    • Handle: RePEc:taf:amstat:v:77:y:2023:i:3:p:331-339
    DOI: 10.1080/00031305.2022.2163689
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