Oracle Cloud Infrastructure Data Science Professional — Lesson 1: Data Science Architecture, Projects, Networking, IAM, Compute, and Workspaces

Learning Objectives

• Explain the purpose and operating context of OCI Data Science service components, project organization, and tenancy design.
• Apply private networking, service gateways, Object Storage, and data access and dynamic groups, policies, resource principals, Vault, and collaboration in a controlled environment.
• Validate outcomes and troubleshoot risks involving shapes, block storage, conda, Git, quotas, cost, and workspace standards.

Detailed Topics

• OCI Data Science service components, project organization, and tenancy design
• private networking, service gateways, Object Storage, and data access
• dynamic groups, policies, resource principals, Vault, and collaboration
• shapes, block storage, conda, Git, quotas, cost, and workspace standards

Instructor Demonstration

Demonstrate OCI Data Science service components, project organization, and tenancy design for an enterprise model portfolio designed for secure collaboration, repeatable delivery, and monitored inference; 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 exercise covering private networking, service gateways, Object Storage, and data access and dynamic groups, policies, resource principals, Vault, and collaboration; introduce an exception involving shapes, block storage, conda, Git, quotas, cost, and workspace standards, 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 OCI Data Science service components, project organization, and tenancy design, private networking, service gateways, Object Storage, and data access, dynamic groups, policies, resource principals, Vault, and collaboration, shapes, block storage, conda, Git, quotas, cost, and workspace standards 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 Data Science Architecture, Projects, Networking, IAM, Compute, and Workspaces in a realistic Oracle Cloud Infrastructure Data Science Professional scenario and defend the result with documented evidence.