Reproducible Analytical Pipelines (RAP) learning pathway
Reproducible Analytical Pipelines (RAP) focus on the use of open-source analytical tools and a variety of techniques from various fields such as software development, software engineering, analytics and collaboration, in order to deliver reproducible, testable and auditable analysis pipelines. This pathway aims at providing a high-level understanding and hands on practice with open-source tools and approaches to develop automated high-quality reproducible research practices while removing any manual / semi-manual processes.
- Demonstrate a hands-on understanding of open-source analytical tools for developing reusable automated data pipelines.
- Use Git to track changes made to the code in a collaborative development environment.
- Test, document and package code in R and Python using reproducible programming techniques.
- Understand the role of a continuous integration pipeline towards development, testing and integration automation in a collaborative environment.
This pathway contains eight courses.
To help decide if this is the pathway for you, this learning persona is designed to create a realistic representation of the intended learning audience.
Sarah is an analyst who joined the analysis team a year ago.
Sarah attended an introduction to R course a year ago and has been using R in her work ever since. Sarah is able to code, but she finds it hard to repeat and remember parts of her analysis months later.
Sarah produces the same quarterly report and would like to automate this and make quality assurance easier. She would like her colleagues to be able to use her code.
Sarah doesn’t have a lot of time to invest in learning and would prefer not to read large amounts of text.
This learning journey will help participants gain the technical tools and familiarity with best practice necessary to transform their work into RAPs.
Enrol on this pathway
You can follow this learning pathway on the Learning Hub.
If you do not have a Learning Hub account, please contact Data.Science.Campus.Faculty@ons.gov.uk.