Oracle Cloud Infrastructure Data Science Professional: Hands-On Workshop — Lesson 2: Data Access, Exploration, Feature Engineering, and Data Preparation

Learning Objectives

• Explain the purpose and operating context of Object Storage, Autonomous Database, file, and dataset connectivity.
• Apply profiling, missing values, outliers, leakage, sampling, and splits and ADS transformations, categorical encoding, scaling, and feature selection in a controlled environment.
• Validate outcomes and troubleshoot risks involving versioned datasets, lineage, quality evidence, and repeatable preparation.

Detailed Topics

• Object Storage, Autonomous Database, file, and dataset connectivity
• profiling, missing values, outliers, leakage, sampling, and splits
• ADS transformations, categorical encoding, scaling, and feature selection
• versioned datasets, lineage, quality evidence, and repeatable preparation

Instructor Demonstration

Demonstrate Object Storage, Autonomous Database, file, and dataset connectivity for a customer-churn model developed in OCI Data Science and promoted through a controlled deployment; 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 Cloud Infrastructure Data Science Professional: Hands-On Workshop exercise covering profiling, missing values, outliers, leakage, sampling, and splits and ADS transformations, categorical encoding, scaling, and feature selection; introduce an exception involving versioned datasets, lineage, quality evidence, and repeatable preparation, 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 Object Storage, Autonomous Database, file, and dataset connectivity, profiling, missing values, outliers, leakage, sampling, and splits, ADS transformations, categorical encoding, scaling, and feature selection, versioned datasets, lineage, quality evidence, and repeatable preparation 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 Access, Exploration, Feature Engineering, and Data Preparation in a realistic Oracle Cloud Infrastructure Data Science Professional: Hands-On Workshop scenario and defend the result with documented evidence.