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

Serveh Sharifi Far, Vanda Inacio de Carvalho, Daniel Paulin, Miguel de Carvalho, Nicole Augustin, Michael Allerhand, Gail Robertson

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.

Original languageEnglish
JournalThe American Statistician
Early online date11 Jan 2023
DOIs
Publication statusE-pub ahead of print - 11 Jan 2023

Keywords / Materials (for Non-textual outputs)

  • Case study
  • Consultancy skills
  • Curriculum design
  • Statistical leadership
  • Workforce preparation

Fingerprint

Dive into the research topics of 'Consultancy Style Dissertations in Statistics and Data Science: Why and How'. Together they form a unique fingerprint.

Cite this