Become a Microsoft Certified Data Analyst Associate (PL-900 + PL-300)

PL-900: Microsoft Power Platform fundamentals (2 days)

Module 1: describing the business value of Microsoft Power Platform

• Introduction to Microsoft Power Platform and its components: Power BI, Power Apps, Power Automate, Power Pages and Microsoft Copilot Studio.
• Business benefits and real organisational use cases.

Module 2: managing the Microsoft Power Platform environment

• How Microsoft Dataverse works: tables, columns, relationships and data security.
• Power Platform connectors, environments and AI Builder capabilities.

Labs

• Create a Dataverse database, integrate an existing table, create a new table and establish data relationships.

Module 3: demonstrating Power Apps capabilities

• Designing canvas and model-driven applications from templates.
• Business use cases and application design best practices.

Module 4: demonstrating Power Automate capabilities

• Business process automation principles with cloud flows.
• Creating automated flows and security flows.

Labs

• Create an automated notification flow and a security flow with Power Automate.

Module 5: demonstrating Power Pages capabilities

• Designing external websites with Power Pages.
• Customising pages, components and themes.

Labs

• Create and customise a simple Power Pages site.

Module 6: exploring complementary Power Platform solutions

• Creating intelligent conversational agents with Microsoft Copilot Studio.
• Configuring topics, customising the greeting and publishing the agent online.

Labs

• Create a conversational agent with Microsoft Copilot Studio and publish it online.

PL-300: Microsoft Power BI data analysis (3 days)

Module 1: the data analyst role and Power BI ecosystem

• The data analyst's role and activities within an organisation.
• Overview of Power BI tools and services: Power BI Desktop, Power BI service and Power BI Mobile.

Module 2: preparing data

• Collecting data from multiple sources and connecting through Power Query.
• Resolving data conflicts and import errors.
• Optimising connection query performance.

Labs

• Connect, clean and process data from multiple sources.

Module 3: cleaning, transforming and loading data

• Structuring data and improving quality.
• Data profiling and anomaly detection.

Labs

• Process, transform and import data in Power BI Desktop.

Module 4: modelling data

• Data modelling principles: tables, relationships and hierarchies.
• Sizing techniques and managing many-to-many relationships.
• Implementing row-level security (RLS).

Labs

• Build a data model with many-to-many relationships and implement row-level security.

Module 5: creating measures with DAX

• Fundamentals of Data Analysis Expressions (DAX) and the DAX editor.
• Using CALCULATE and Time Intelligence functions.
• Creating measures and key performance indicators.

Labs

• Create calculated tables, columns and measures with DAX, including Time Intelligence functions.

Module 6: optimising data model performance

• Optimising data models and DirectQuery models.
• Creating and processing aggregations to improve performance.

Module 7: designing and optimising reports

• Designing interactive reports with relevant visuals.
• Implementing filters, synchronised slicers and conditional formatting.
• Optimising report performance and accessibility.

Labs

• Design an interactive report with synchronised slicers, conditional formatting and watermarks.

Module 8: creating dashboards

• Creating static and dynamic dashboards.
• Configuring alerts and using Q&A features.

Labs

• Create an interactive dashboard with alerts and a Q&A window.

Module 9: creating paginated reports

• Designing paginated reports with Power BI Report Builder.
• Configuring data sources, parameters and PDF export.

Labs

• Create and publish a multi-page paginated report with Power BI Report Builder.

Module 10: performing advanced analysis

• Exploring automated insights with AI-powered visuals.
• Time-series analysis and outlier detection.
• Using key influencers and the decomposition tree.

Labs

• Perform advanced analysis using Power BI AI visuals.

Module 11: managing workspaces

• Creating and organising collaborative workspaces.
• Sharing dashboards and distributing apps.

Labs

• Create a workspace, distribute an app and manage its security protocols.

Module 12: managing semantic models

• Configuring and managing semantic models.
• Resolving gateway connectivity problems.

Module 13: securing Power BI data and resources

• Securing resources and semantic models.
• Configuring row-level security and managing user subscriptions.