Data Carpentry’s Post-Workshop Survey Report

January 2018

This analysis was completed using Data Carpentry’s post-workshop surveys collected August 7, 2017 through January 8, 2018. A PDF of the survey questions, the data used in this analysis, and full R code are located on the Carpentries assessment repo on GitHub. Feel free to use the data and tell us about your findings.

Workshop Location

The majority of respondents attended workshops in the United States, however, we see representation in multiple countries including Canada, Ethiopia, South Africa, and Switzerland.

Country n %
Albania 2 0.8
Bahamas 1 0.4
Canada 23 8.7
Ethiopia 27 10.2
Netherlands 7 2.7
Poland 1 0.4
South Africa 42 15.9
Switzerland 17 6.4
United Kingdom 9 3.4
United States of America 135 51.1

Workshop Attended and Tools Covered

Respondents were asked which workshop they attended: Ecology, Genomics, Geospatial, Reproducible Research, or Social Sciences. The majority of respondents (53%) attended an Ecology workshop.

Workshop n %
Ecology 166 52.7
Genomics 52 16.5
Geospatial 2 0.6
Reproducible Research 31 9.8
Social Sciences 7 2.2
I don’t know. 57 18.1

As the majority of workshop respondents attended an Ecology workshop, it is no surprise that R was covered in most workshops.

ToolCovered n %
R 223 76.1
Python 44 15.0
Neither 22 7.5
I don’t know./I don’t remember. 4 1.4

Perception of Workshop Impact

Learners were asked to rate their level of agreement with the following statements related to Data Carpentry’s workshop goals and learning objectives. The figure below provides a visual representation of their responses. Axis labels and the corresponding question are as follows:

  • WriteProgram: I can write a small program/script/macro to solve a problem in my own work.
  • TechnicalQuestions: I know how to search for answers to my technical questions online.
  • RawData: Having access to the original, raw data is important to be able to repeat an analysis.
  • ProgrammingEasier: Using a programming language (like R or Python) can make my analyses easier to reproduce.
  • OvercomeProblem: While working on a programming project, if I get stuck, I can find ways of overcoming the problem.
  • ImmediatelyApply: I can immediately apply what I learned at this workshop.
  • ConfidentProgramming: I am confident in my ability to make use of programming software to work with data.
  • ConfidenceSoftware: Using a programming language (like R or Python) can make me more efficient at working with data.
  • ComfortableLearning: I felt comfortable learning in this workshop environment.