Introduction to Reproducibility

Version 1

What is a reproducible analytical pipeline (RAP), writing “good code” and creating a reproducible report.

Course objectives

Participants should gain an awareness of the importance of reproducibility in their work. Learners will also gain experience of linting code in Python and using parameterised reports in R markdown.

Learning objectives

  • Represent pipelines and identify opportunities to automate;
  • Consider adherence to a programming style guide;
  • Use linting software to standardise Python scripts;
  • Use parameterised R markdown reports to improve the efficiency of report production.

Course type

E learning – Not available

Self learning – Not available

Face to face – Available

Skill level

Basic familiarity with Python and R programming is assumed.

Booking

To discuss booking this course for remote delivery, please contact the Data Science Campus Faculty.