Down the rabbit hole. A 101.1 on reproducible workflows with
Practical reproducibility steps for data science and research
Tuesday 18th, 14:30 (Room C)
Tuesday 18th, 11:30 (Room C)
There has been a massive interest in reproducible research / data
analysis pipelines over the last few years.
But... how can I ensure that what I produce as a Python
user is reproducible?
In this tutorial, we'll be taking you on a journey down the rabbit hole
We'll focus on practical steps that you can perform to ensure reproducibility in your data analysis pipelines.
This means you get a crash course on data management, licensing, Python packaging creation, testing (data and code), continuous integration and execution environments. Plus a guide on how to integrate all of these in your data projects.
By the end of the course, we hope you will have the necessary tools to make your
Python workflows reproducible no matter if you're starting a brand new project
or if this is ready to be shared with the world.
- The speaker suggested this session is suitable for new programmers.
- The speaker suggested this session is suitable for teachers.
- The speaker suggested this session is suitable for data scientists.