Data Visualization on Oracle Analytics Cloud

Course Description

Data Visualization on Oracle Analytics Cloud builds practical capability across Analytics Cloud interface, data sources, datasets, preparation, Visualization grammar, canvases, filters. Rather than treating the application as a set of isolated screens, the course follows the decisions, dependencies, roles, and evidence needed to make the process dependable in day-to-day operations.

The learning journey then moves into Augmented analytics, explain, forecasting, machine learning, Performance, permissions, row-level security. Activities use prepared dataset and data-quality profile, interactive visualization workbook and design rationale, augmented analysis and shared insight narrative, visualization quality checklist and governed catalog entry to compare expected behaviour with actual output, isolate defects early, and document corrective decisions without losing traceability.

By completion, participants can contribute to clear, performant, and secure Oracle Analytics Cloud visualizations that turn prepared data into defensible business insight. The final improvement plan identifies practical follow-up actions for monitoring, ownership, exception handling, support readiness, and controlled change.

What You Will Learn

• Explain how Analytics Cloud interface, data sources, and datasets establish the operating foundation and shape later configuration decisions.
• Configure preparation, profiling, joins, and semantic considerations, then confirm readiness through prepared dataset and data-quality profile rather than relying on screen messages alone.
• Execute a controlled workflow covering Visualization grammar, canvases, and filters, preserving input traceability, approvals, and the resulting changes.
• Read interactive visualization workbook and design rationale to isolate a calculated measure changing with filter context unexpectedly, separate symptoms from causes, and select a correction that can be safely retested.
• Integrate Augmented analytics, explain, and forecasting with ownership, access, and downstream process expectations.
• Challenge machine learning, mobile use, and collaboration using normal and boundary cases, then defend the outcome with augmented analysis and shared insight narrative.
• Investigate a shared workbook exposing restricted regional data by reviewing Performance, permissions, and row-level security before applying a reversible fix.
• Prepare visualization quality checklist and governed catalog entry that demonstrates publishing, governance, accessibility, and adoption and supports handover, monitoring, and the next improvement decision.

SkillDynamo Training Approach

Delivery combines focused demonstrations, guided build activities, scenario comparison, evidence review, fault isolation, peer discussion, and a final improvement plan. Each activity requires learners to record assumptions, decisions, expected results, observed behaviour, and the owner of any unresolved action.