Oracle Machine Learning for R — Lesson 2: Database-Resident Data Manipulation, Statistics, Visualization, and Preparation

Learning Objectives

• Explain the purpose and operating context of selection, filtering, joins, aggregation, ordering, and derived variables.
• Apply descriptive statistics, distributions, correlations, and sampling and visualization choices, local versus database data, and memory considerations in a controlled environment.
• Validate outcomes and troubleshoot risks involving missing values, encoding, transformations, feature preparation, and lineage.

Detailed Topics

• selection, filtering, joins, aggregation, ordering, and derived variables
• descriptive statistics, distributions, correlations, and sampling
• visualization choices, local versus database data, and memory considerations
• missing values, encoding, transformations, feature preparation, and lineage

Instructor Demonstration

Demonstrate selection, filtering, joins, aggregation, ordering, and derived variables for an R analytics workflow scaled through Oracle Database while preserving reproducibility and access controls; 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 Machine Learning for R exercise covering descriptive statistics, distributions, correlations, and sampling and visualization choices, local versus database data, and memory considerations; introduce an exception involving missing values, encoding, transformations, feature preparation, and lineage, 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 selection, filtering, joins, aggregation, ordering, and derived variables, descriptive statistics, distributions, correlations, and sampling, visualization choices, local versus database data, and memory considerations, missing values, encoding, transformations, feature preparation, and lineage 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 Database-Resident Data Manipulation, Statistics, Visualization, and Preparation in a realistic Oracle Machine Learning for R scenario and defend the result with documented evidence.