Oracle Cloud Infrastructure Data Science Professional — Lesson 2: Data Engineering, Feature Pipelines, ADS, Datasets, and Experiment Design

Learning Objectives

• Explain the purpose and operating context of database, Object Storage, streaming awareness, and dataset connectivity.
• Apply profiling, data quality, leakage, sampling, splits, and transformations and ADS datasets, feature engineering, reproducibility, and lineage in a controlled environment.
• Validate outcomes and troubleshoot risks involving experiments, metadata, version control, environment capture, and evidence.

Detailed Topics

• database, Object Storage, streaming awareness, and dataset connectivity
• profiling, data quality, leakage, sampling, splits, and transformations
• ADS datasets, feature engineering, reproducibility, and lineage
• experiments, metadata, version control, environment capture, and evidence

Instructor Demonstration

Demonstrate database, Object Storage, streaming awareness, and dataset connectivity 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 profiling, data quality, leakage, sampling, splits, and transformations and ADS datasets, feature engineering, reproducibility, and lineage; introduce an exception involving experiments, metadata, version control, environment capture, and evidence, 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 database, Object Storage, streaming awareness, and dataset connectivity, profiling, data quality, leakage, sampling, splits, and transformations, ADS datasets, feature engineering, reproducibility, and lineage, experiments, metadata, version control, environment capture, and evidence 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 Engineering, Feature Pipelines, ADS, Datasets, and Experiment Design in a realistic Oracle Cloud Infrastructure Data Science Professional scenario and defend the result with documented evidence.