Oracle Cloud Infrastructure Data Science Professional: Hands-On Workshop — Lesson 1: OCI Data Science Projects, Notebook Sessions, Conda, ADS, and Access

Learning Objectives

• Explain the purpose and operating context of projects, notebook-session shapes, block volumes, networking, and lifecycle.
• Apply conda environments, package management, Git, storage, and reproducibility and ADS data access, OCI authentication, resource principals, and policies in a controlled environment.
• Validate outcomes and troubleshoot risks involving workspaces, naming, ownership, cost controls, and environment validation.

Detailed Topics

• projects, notebook-session shapes, block volumes, networking, and lifecycle
• conda environments, package management, Git, storage, and reproducibility
• ADS data access, OCI authentication, resource principals, and policies
• workspaces, naming, ownership, cost controls, and environment validation

Instructor Demonstration

Demonstrate projects, notebook-session shapes, block volumes, networking, and lifecycle for a customer-churn model developed in OCI Data Science and promoted through a controlled deployment; capture the exact configuration, code, command, transaction, query, dashboard, model, schedule, or report evidence that proves the expected result.

Hands-On Exercise

Complete a controlled Oracle Cloud Infrastructure Data Science Professional: Hands-On Workshop exercise covering conda environments, package management, Git, storage, and reproducibility and ADS data access, OCI authentication, resource principals, and policies; introduce an exception involving workspaces, naming, ownership, cost controls, and environment validation, diagnose it from evidence, apply the smallest reversible correction, and rerun acceptance checks.

1. Confirm scope, prerequisites, identities, dependencies, data, risks, and expected outcome.
2. Perform the course-specific configuration, development, administration, analysis, or business-process workflow.
3. Capture commands, settings, records, logs, screenshots, reports, metrics, or test results.
4. Compare the observed result with acceptance criteria and document corrective or preventive action.

Scenario Analysis

• Identify the technical or business decision and the stakeholders affected by it.
• Compare security, performance, reliability, compliance, cost, usability, and support trade-offs.
• Define rollback, recovery, communication, evidence, ownership, and escalation requirements.

Troubleshooting Guidance

Diagnose issues involving projects, notebook-session shapes, block volumes, networking, and lifecycle, conda environments, package management, Git, storage, and reproducibility, ADS data access, OCI authentication, resource principals, and policies, workspaces, naming, ownership, cost controls, and environment validation without making an unverified production change. Separate symptoms from causes, gather time-correlated evidence, test a reversible hypothesis, validate the correction, and record recurrence controls.

Validation Checklist

• The requirement, environment, inputs, dependencies, and expected result are documented.
• The result is reproducible and supported by product-specific evidence.
• Normal, boundary, failure, security, performance, and operational conditions are reviewed.
• Exceptions, rollback, ownership, support actions, and the next improvement priority are recorded.

Lesson Outcome

Learners can apply OCI Data Science Projects, Notebook Sessions, Conda, ADS, and Access in a realistic Oracle Cloud Infrastructure Data Science Professional: Hands-On Workshop scenario and defend the result with documented evidence.