There are twelve lectures in this course. Each lecture is about 1 hour long.
We prepared lectures using Google Slides. We provide links to these resources below.
| Lecture | Topic |
|---|---|
| 1.Introduction | Introduction to the course and computational reproducibility |
| 2.Version control | Version control best practices and mindset |
| 3.Environments | Computational environments |
| 4.Data wrangling | Tidy data princples and software |
| 5.Workflows and orchestration | Workflow mindset and management |
| 6.Data visualization | Tips, tricks, and principles for data visualization |
| 7.Interactive data anlaysis | Concepts and pratical tips for interactive data analysis |
| 8.Documentation and readability | Tips and tricks on how to write effective documentation |
| 9.R and Python Packaging | R and Python packaging |
| 10.HPC and Parallel Computing | Hardware, parallel computing, and Alpine HPC |
| 11.Software Gardening | Mindset for sustaining code and projects |
| 12.Reproducibility as an Iterative Process | Course recap and putting it all together |