Building on Data Best Practices: Basics, this one-week course covers advanced topics in reproducible, portable, and scalable scientific computing. Students will containerize software and environments with Podman/Docker; parallelize analyses locally with GNU Parallel and orchestrate cloud-based, asynchronous parallelization with Google Cloud Pub/Sub; and implement CI/CD in Stanford GitLab, using runners to automate scientific data preprocessing and analysis pipelines. It is recommended that students take Data Best Practices: Basics prior to enrolling in this course. Students will equivalent experience may also enroll.
1 units · Medical Option (Med-Ltr-CR/NC)
Building on Data Best Practices: Basics, this one-week course covers advanced topics in reproducible, portable, and scalable scientific computing. Students will containerize software and environments with Podman/Docker; parallelize analyses locally with GNU Parallel and orchestrate cloud-based, asynchronous parallelization with Google Cloud Pub/Sub; and implement CI/CD in Stanford GitLab, using runners to automate scientific data preprocessing and analysis pipelines. It is recommended that students take Data Best Practices: Basics prior to enrolling in this course. Students will equivalent experience may also enroll.
Offered in Winter 2027 at Stanford University.