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Expert3 to 5 days / 20+ guided hourspublished
Data Sciences: Expert
Design a production analytics workflow with versioning, performance profiling, governance, and a capstone model.
Audience
- Self-paced technical learners
- Instructor-led cohorts
- Enterprise teams preparing staff for hands-on operations
Prerequisites
- Intermediate course or equivalent production experience
- Comfort with troubleshooting and design tradeoffs
Outcomes
- Provision an isolated data sciences lab from template metadata.
- Use snapshots, rollback, validation checks, and teardown safely.
- Explain how Python, Jupyter, PostgreSQL fit into an enterprise training environment.
- Produce evidence that an instructor or admin can review.
Course plan
Modules and labs
Each module maps to provisioned lab work, validation evidence, reset/rollback policy, and instructor visibility.
Required templates
Python data science workstation
definedUbuntu 24.04 LTS
JupyterLab server
definedUbuntu 24.04 LTS
PostgreSQL database
definedUbuntu 24.04 LTS
Validation checks
Notebook reachable
JupyterLab returns a valid login page or authenticated health response.
VM reachable
The VM reports boot complete and responds through the tenant bastion path.