Oracle Cloud Infrastructure Data Science Professional (2025): Hands-on Workshop — Lesson 1: OCI Data Science projects, notebook sessions, conda environments, Accelerated Data Science SDK, object storage access, repositories, and IAM

Learning Objectives

• Translate a realistic requirement into an executable approach for OCI Data Science projects, notebook sessions, conda environments, Accelerated Data Science SDK, object storage access, repositories, and IAM.
• Use working notebook environment and reproducibility record to verify completeness, access, and process behaviour.
• Explain how to recover safely when a notebook running for its creator but failing for another user because resource-principal access is incomplete occurs.

Detailed Topics

• OCI Data Science projects
• Notebook sessions
• Conda environments
• Accelerated Data Science SDK
• Object storage access
• Repositories
• And IAM
• Decision ownership, prerequisites, and acceptance evidence for OCI Data Science projects, notebook sessions, conda environments, Accelerated Data Science SDK, object storage access, repositories, and IAM
• Relevant access, data, integration, performance, recovery, and support considerations

Instructor Demonstration

The demonstration traces OCI Data Science projects, notebook sessions, conda environments, Accelerated Data Science SDK, object storage access, repositories, and IAM from requirement through execution and review. Learners see where evidence should be collected, how working notebook environment and reproducibility record supports verification, and which warning signs demand investigation.

Hands-On Exercise

Complete a guided task covering OCI Data Science projects, notebook sessions, conda environments, Accelerated Data Science SDK, object storage access, repositories, and IAM. After establishing a working baseline, reproduce “a notebook running for its creator but failing for another user because resource-principal access is incomplete,” isolate the responsible setting or data condition, apply a reversible fix, and update working notebook environment and reproducibility record with before-and-after proof.

1. Define the requirement and identify dependencies before changing the environment.
2. Create a baseline result that can be compared with later changes.
3. Reproduce the exception and test one hypothesis at a time.
4. Complete the evidence record and obtain peer or instructor review.

Scenario Analysis

• Clarify the business trigger, affected roles, required records, and acceptable result for OCI Data Science projects, notebook sessions, conda environments, Accelerated Data Science SDK, object storage access, repositories, and IAM.
• Compare at least two implementation or correction choices and identify which evidence would distinguish them.
• Decide how working notebook environment and reproducibility record should be reviewed, approved, retained, and handed to the next owner.

Troubleshooting Guidance

Avoid editing multiple settings at once when troubleshooting OCI Data Science projects, notebook sessions, conda environments, Accelerated Data Science SDK, object storage access, repositories, and IAM. Preserve the failing example, review working notebook environment and reproducibility record, and verify whether “a notebook running for its creator but failing for another user because resource-principal access is incomplete” is the root cause. After remediation, repeat the transaction, confirm downstream effects, and record any monitoring action.

Validation Checklist

• Prerequisites and role permissions for OCI Data Science projects, notebook sessions, conda environments, Accelerated Data Science SDK, object storage access, repositories, and IAM have been confirmed before execution.
• The result agrees with the expected values documented in working notebook environment and reproducibility record.
• Exceptions are explained with evidence rather than hidden by manual correction.
• The final record names the approver, monitoring point, and next improvement action.

Lesson Outcome

Learners can apply OCI Data Science projects, notebook sessions, conda environments, Accelerated Data Science SDK, object storage access, repositories, and IAM, explain the decisions made, and defend the result with working notebook environment and reproducibility record.