Enhanced Visual Analysis with Data Visualization Ed 1 — Lesson 2: Visual Grammar, Canvas Design, Filters, Interactions, and Calculations

Technical Scope

• visualization selection based on analytical question and data shape
• canvas layouts, properties, color use, labels, reference lines, and accessibility
• filters, controls, brushing, selection steps, and cross-visual interactions
• calculated columns, measures, bins, groups, parameters, and reusable logic

Instructor Demonstration

Demonstrate visualization selection based on analytical question and data shape within an analyst turning a mixed sales-and-service dataset into an exploratory workbook that reveals drivers, anomalies, segments, and decisions without misleading the audience, then capture the exact configuration, command, transaction, report, trace, or code evidence that proves the expected result.

Applied Workshop

Implement a controlled workshop covering canvas layouts, properties, color use, labels, reference lines, and accessibility and filters, controls, brushing, selection steps, and cross-visual interactions, then test a failure, exception, or business-control scenario involving calculated columns, measures, bins, groups, parameters, and reusable logic.

1. Confirm the environment, identities, data, dependencies, controls, and expected result.
2. Complete the product-specific configuration, development, analysis, or business-process task.
3. Capture commands, configuration, output, logs, transactions, reports, or test evidence.
4. Explain the operational, security, performance, data-quality, or business trade-off.

Validation Standard

The Visual Grammar, Canvas Design, Filters, Interactions, and Calculations result must be reproducible, traceable to approved inputs, and consistent with expected Oracle product or business-process behavior.

• The intended target, scope, and ownership are verified.
• The result is supported by reproducible evidence and expected-value checks.
• Security, performance, reliability, data quality, and support impact are reviewed.
• Unexpected behavior is investigated before acceptance or release.

Failure Analysis

Diagnose issues in visualization selection based on analytical question and data shape, canvas layouts, properties, color use, labels, reference lines, and accessibility, filters, controls, brushing, selection steps, and cross-visual interactions, or calculated columns, measures, bins, groups, parameters, and reusable logic without making an unverified production change.

Learners isolate the smallest proven cause, apply a reversible correction, rerun validation, and record the preventive action.

Practical Outcome

Complete a validated implementation or analysis for Visual Grammar, Canvas Design, Filters, Interactions, and Calculations and defend the result with documented evidence.