Integrating Data Science Tools into a Graduate Level Data Management Course

Pete Pascuzzi, Megan Sapp Nelson

Research output: Contribution to journalArticlepeer-review


Objective: This paper describes a project to revise an existing research data management (RDM) course to include instruction in computer skills with robust data science tools.

Setting: A Carnegie R1 university.

Brief Description: Graduate student researchers need training in the basic concepts of RDM. However, they generally lack experience with robust data science tools to implement these concepts holistically. Two library instructors fundamentally redesigned an existing research RDM course to include instruction with such tools. The course was divided into lecture and lab sections to facilitate the increased instructional burden. Learning objectives and assessments were designed at a higher order to allow students to demonstrate that they not only understood course concepts but could use their computer skills to implement these concepts.

Results: Twelve students completed the first iteration of the course. Feedback from these students was very positive, and they appreciated the combination of theoretical concepts, computer skills and hands-on activities. Based on student feedback, future iterations of the course will include more “flipped” content including video lectures and interactive computer tutorials to maximize active learning time in both lecture and lab.
Original languageEnglish (US)
Pages (from-to)e1152
JournalJournal of eScience Librarianship
Issue number3
StatePublished - Dec 20 2018
Externally publishedYes


  • data information literacy
  • Unix
  • Excel
  • RStudio
  • R
  • data science
  • research data management
  • STEM education
  • graduate education
  • active learning
  • backwards design
  • flipped instruction


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