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Course Outline

Introduction

Overview of Microsoft Power Platform Features

  • Power Platform main components.
  • Integration with Office 365, Dynamics 365, and third-party apps.
  • Data sources and connectors.

Getting Started with Microsoft Power Platform

  • Microsoft Dataverse basics (formerly Common Data Service (CDS)).
  • Tables and columns.
  • Relationships and environments.
  • Business rules.
  • Power Platform admin center.

Building Simple Applications with Power Apps

  • Connecting with common data sources.
  • Different app types.
  • No code artificial intelligence (AI).
  • Security, governance, and reporting features.
  • Power Apps Portals.

Creating Different Applications with Power Apps

  • Building a canvas app.
  • Basic elements and functions.
  • Connecting to a data source.
  • Creating a model-driven app.
  • Building blocks (data, user interface, logic, visualization).
  • Creating a form.
  • Security and access control.

Automating Processes with Power Automate

  • Flow types.
  • Flow templates.
  • Recurring flows.
  • Button flow.
  • Approval requests.
  • Business process flow.

Generating Reports and Dashboards with Power BI

  • Power BI parts, concepts, and capacities.
  • Components (workspaces, datasets, reports, dashboards).
  • Template apps.
  • Visualizations (charts, KPIs, maps, matrices, etc.).
  • Data filters and buttons.
  • Transforming and cleaning data.
  • Working with aggregates.
  • Security and administration.
  • Building a simple dashboard.

Creating Chatbots with Power Virtual Agents

  • Key components.
  • Creating a chatbot.
  • Working with topics.
  • Testing and publishing.
  • Analyzing a chatbot.

Exploring Advanced Power Platform Topics

  • Administration guide.
  • Application lifecycle management.
  • Power Platform best practices.
  • AI Builder.

Troubleshooting

Summary and Conclusion

Requirements

  • A general understanding of Microsoft Office 365 and Dynamics 365 concepts.
  • Familiarity with app development, workflow automation, and data analysis.

Audience

  • Business staff.
  • Managers.
  • Developers.
  • Data analysts.
 14 Hours

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