Using Oracle Machine Learning with Autonomous Database 2024 ELS — Lesson 2: Data Exploration, Preparation, Feature Engineering, and SQL-Based Analytics

Learning Objectives

• Trace how data exploration, preparation, feature engineering, and sql-based analytics changes application behaviour, data, accounting, or service delivery.
• Configure a repeatable sequence with known inputs, controls, outputs, and rollback points.
• Defend security, correctness, performance, recoverability, and support readiness.

Detailed Topics

• Data Exploration
• Preparation
• Feature Engineering
• SQL-Based Analytics
• ownership, prerequisites, data, and acceptance criteria for data exploration, preparation, feature engineering, and sql-based analytics
• security, control, integration, performance, recovery, and support implications

Instructor Demonstration

Demonstrate data exploration, preparation, feature engineering, and sql-based analytics within an analytics team building and operationalizing an in-database machine-learning model without exporting governed enterprise data. Use representative data and capture feature transformations, model metrics so another practitioner can verify the result.

Hands-On Exercise

Complete a controlled Using Oracle Machine Learning with Autonomous Database 2024 ELS activity focused on data exploration, preparation, feature engineering, and sql-based analytics. Introduce a realistic case of poor feature quality, isolate its cause, apply a reversible correction, and rerun the acceptance checks.

1. Record the requirement, environment, identities, dependencies, data, controls, and expected result.
2. Perform the workflow and capture course-specific evidence before and after each material change.
3. Test normal, boundary, failure, security, reconciliation, recovery, and operational conditions.
4. Document the root cause, correction, rollback, owner, support action, and next improvement priority.

Scenario Analysis

• Separate mandatory requirements from optional convenience and local workaround requests.
• Compare the proposed approach against security, reliability, usability, cost, and audit expectations.
• Select the smallest supportable change and assign follow-up ownership.

Troubleshooting Guidance

When data exploration, preparation, feature engineering, and sql-based analytics does not produce the expected result, confirm scope, effective dates, identities, data context, dependencies, and recent changes. Review the most relevant feature transformations, model metrics before editing configuration. Test one reversible hypothesis at a time, verify recovery, and confirm that the fix does not create poor feature quality elsewhere.

Validation Checklist

• Prerequisites, ownership, data context, settings, and expected outcomes are documented.
• The result is reproducible and supported by feature transformations, model metrics.
• Security, controls, data quality, integration, performance, recovery, and support conditions are reviewed.
• Exceptions, corrective action, rollback, approvals, monitoring, and handover are recorded.

Lesson Outcome

Learners can apply Data Exploration, Preparation, Feature Engineering, and SQL-Based Analytics in a realistic Using Oracle Machine Learning with Autonomous Database 2024 ELS situation and defend the result with course-specific evidence.