Implement Machine Learning Using Oracle Data Miner — Lesson 2: Explore, Transform, Sample, Partition, and Prepare Data

Learning Objectives

• Explain the purpose and operating context of explore-data nodes, distributions, statistics, correlations, and plots.
• Apply missing values, outliers, normalization, binning, and categorical handling and sampling, train and test partitioning, target balance, and leakage prevention in a controlled environment.
• Validate outcomes and troubleshoot risks involving transformation nodes, reusable preparation, lineage, and quality evidence.

Detailed Topics

• explore-data nodes, distributions, statistics, correlations, and plots
• missing values, outliers, normalization, binning, and categorical handling
• sampling, train and test partitioning, target balance, and leakage prevention
• transformation nodes, reusable preparation, lineage, and quality evidence

Instructor Demonstration

Demonstrate explore-data nodes, distributions, statistics, correlations, and plots for a Data Miner workflow created from raw customer data through deployable scoring SQL; 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 Implement Machine Learning Using Oracle Data Miner exercise covering missing values, outliers, normalization, binning, and categorical handling and sampling, train and test partitioning, target balance, and leakage prevention; introduce an exception involving transformation nodes, reusable preparation, lineage, and quality 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 explore-data nodes, distributions, statistics, correlations, and plots, missing values, outliers, normalization, binning, and categorical handling, sampling, train and test partitioning, target balance, and leakage prevention, transformation nodes, reusable preparation, lineage, and quality 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 Explore, Transform, Sample, Partition, and Prepare Data in a realistic Implement Machine Learning Using Oracle Data Miner scenario and defend the result with documented evidence.