Oracle Machine Learning for Python on Oracle Cloud — Lesson 2: Data Manipulation, Exploration, Visualization, and Feature Preparation

Learning Objectives

• Explain the purpose and operating context of proxy-object selection, filtering, joins, grouping, and aggregation.
• Apply descriptive statistics, correlation, sampling, and exploratory analysis and missing values, transformations, encoding, scaling, and feature generation in a controlled environment.
• Validate outcomes and troubleshoot risks involving performance trade-offs, repeatability, lineage, and prepared-data checks.

Detailed Topics

• proxy-object selection, filtering, joins, grouping, and aggregation
• descriptive statistics, correlation, sampling, and exploratory analysis
• missing values, transformations, encoding, scaling, and feature generation
• performance trade-offs, repeatability, lineage, and prepared-data checks

Instructor Demonstration

Demonstrate proxy-object selection, filtering, joins, grouping, and aggregation for a Python data-science workflow moved from local memory to in-database execution and governed scoring; 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 Python on Oracle Cloud exercise covering descriptive statistics, correlation, sampling, and exploratory analysis and missing values, transformations, encoding, scaling, and feature generation; introduce an exception involving performance trade-offs, repeatability, lineage, and prepared-data checks, 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 proxy-object selection, filtering, joins, grouping, and aggregation, descriptive statistics, correlation, sampling, and exploratory analysis, missing values, transformations, encoding, scaling, and feature generation, performance trade-offs, repeatability, lineage, and prepared-data checks 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 Manipulation, Exploration, Visualization, and Feature Preparation in a realistic Oracle Machine Learning for Python on Oracle Cloud scenario and defend the result with documented evidence.