Oracle Database 19c: Data Mining Techniques — Lesson 1: Mining Problems, Data Preparation, Sampling, Transformations, and Leakage Control

Learning Objectives

• Explain the architecture, workflow, or business purpose behind business questions, target definitions, supervised and unsupervised tasks.
• Apply source tables, keys, target balance, sampling, and train or test separation and missing values, outliers, binning, normalization, and categorical handling in a controlled scenario.
• Validate outcomes and troubleshoot risks involving data leakage, time boundaries, quality evidence, and preparation records.

Detailed Topics

• business questions, target definitions, supervised and unsupervised tasks
• source tables, keys, target balance, sampling, and train or test separation
• missing values, outliers, binning, normalization, and categorical handling
• data leakage, time boundaries, quality evidence, and preparation records

Instructor Demonstration

Demonstrate business questions, target definitions, supervised and unsupervised tasks within a customer-risk dataset modeled and scored inside Oracle Database with transparent validation and deployment controls, then capture the exact configuration, command, transaction, report, model, query, code, or process evidence that proves the result for lesson 1.

Hands-On Exercise

Implement a controlled exercise covering source tables, keys, target balance, sampling, and train or test separation and missing values, outliers, binning, normalization, and categorical handling, then introduce and resolve an exception involving data leakage, time boundaries, quality evidence, and preparation records while preserving before-and-after evidence.

1. Confirm the environment, identities, inputs, dependencies, risks, and expected outcome.
2. Complete the course-specific configuration, development, analysis, or process workflow.
3. Capture settings, commands, data, outputs, logs, reports, screenshots, calculations, or test results.
4. Compare the observed result with acceptance criteria and document corrective action.

Scenario Analysis

• Identify the technical or business decision that must be made.
• Compare valid approaches and their security, performance, reliability, cost, compliance, and support trade-offs.
• Define rollback, recovery, ownership, communication, and evidence requirements.

Troubleshooting Guidance

Diagnose issues across business questions, target definitions, supervised and unsupervised tasks, source tables, keys, target balance, sampling, and train or test separation, missing values, outliers, binning, normalization, and categorical handling, data leakage, time boundaries, quality evidence, and preparation records without making an unverified production change. Separate symptoms from causes, gather evidence, test the smallest reversible correction, rerun validation, and record preventive action.

Validation Checklist

• The requirement, scope, inputs, dependencies, and expected result are documented.
• The result is reproducible and supported by course-specific evidence.
• Normal, boundary, failure, security, performance, and operational conditions are reviewed.
• Exceptions, rollback, ownership, and the next improvement priority are recorded.

Lesson Outcome

Learners can apply Mining Problems, Data Preparation, Sampling, Transformations, and Leakage Control in a realistic Oracle Database 19c: Data Mining Techniques scenario and validate the result with documented evidence.