FA-0551Data & Analytics

Advanced Power BI: Modelling, Analytics and Automation

A two-day continuation for experienced report creators

Improve Power BI models, measures and interactive reports, then evaluate refresh, AI-assisted analysis and Python extensions.

Introduction

Why this course

This two-day continuation course is for professionals who already create Power BI reports. It develops a clearer semantic model, reusable DAX measures, time-aware analysis and purposeful report interactions, then explores integration, refresh and governed sharing.

Selected exercises use an illustrative reporting scenario. AI-assisted analysis, custom visuals and Python are evaluated with attention to feature availability, security and deployment limits. The course does not promise an unrestricted enterprise-scale or near-real-time system in two days.

Use suitable Power BI Desktop/service access and approved sample data. Copilot exercises depend on supported paid capacity, region and tenant settings; where unavailable, they are demonstrated or discussed. Python hands-on work requires a compatible local environment and approved packages.

Learning outcomes

The course teaches participants to:

Design a star-schema model with appropriate grain, relationships and date handling.
Write and review foundational DAX measures using row/filter context.
Analyse trends and seasonality and evaluate the limitations of built-in forecasting.
Use drill-through, tooltips, bookmarks and navigation for an interactive reporting task.
Plan data integration, refresh and controlled service access within the selected storage mode and entitlements.
Evaluate available AI-assisted features and validate generated analysis or DAX.
Use a guided Python visual or transformation example and identify its security and service-deployment constraints.

Prerequisites

Prior Power BI training or hands-on experience
Ability to build basic reports and visuals
Familiarity with Power Query fundamentals
Basic understanding of data concepts (tables, relationships, dates)
Optional: introductory familiarity with Excel formulas or Python

Power BI Desktop and suitable service permissions for the chosen exercises. Python examples use provided scripts; prior Python is helpful and a configured environment is required for independent hands-on execution.

Training outline

2 modules

01Day 1 — Models, DAX and Time-Aware Reports1 topics

Power BI as an Analytics Platform

Reframing Power BI beyond static reporting
Semantic models and their role in reliable business analysis.
Import vs DirectQuery vs composite models
Common mistakes made after beginner-level Power BI usage
Designing reports for decision-making, not decoration

Data Modelling

Star schema vs flat models
Fact tables, dimension tables, and granularity
Managing relationships and cardinality
Handling date tables and time intelligence foundations
Model performance and optimization considerations

DAX Measures

Understanding row context vs filter context
Measures vs calculated columns
Core DAX functions used in enterprise reports
Building reusable and readable DAX patterns
Common DAX mistakes and how to avoid them

Time Series and Forecasting

Time-aware reporting concepts
Using built-in time intelligence functions
Trend analysis and moving averages
Power BI forecasting capabilities and limitations
Evaluating seasonality and patterns in business data

Advanced Report Interactions

Report-level, page-level, and visual-level interactions
Drill-down, drill-through, and tooltip pages
Dynamic titles, measures, and visuals
Bookmarks, buttons, and storytelling techniques
Designing intuitive navigation for complex reports

02Day 2 — Integration, Governed Extensions and AI1 topics

Custom Visuals

Overview of Power BI custom visuals ecosystem
When to use built-in vs custom visuals
Advanced charting scenarios not covered by defaults
Performance and security considerations with custom visuals
Governance and certification of visuals

Refresh and Data Integration

Integrating Power BI with Dataverse, SQL, and cloud data sources
Scheduled/incremental refresh and dataflows: requirements, storage-mode choices and operational limits.
Evaluate near-real-time requirements and suitable connection/refresh options; do not assume scheduled refresh is real time.
Power BI service architecture and workspace strategies
Sharing, app publishing, and controlled access

AI-Assisted Analysis

Overview of AI features available in Power BI
Copilot-assisted reporting and DAX where supported capacity, region and tenant configuration permit; review generated results.
Available built-in anomaly-detection and key-influencer visuals: supported scenarios and interpretation limits.
Natural-language analysis through available Copilot experiences; discuss legacy Q&A and its announced retirement rather than rely on it for a new long-lived solution.
Responsible and governed use of AI features

Python Extensions

When and why to use Python in Power BI
Python scripts in Power Query for data transformation
Python-generated chart images for selected analytics; distinguish their limited interaction from native Power BI visuals.
Using Python for statistical analysis and forecasting
Package, execution, security, licensing/region and service-refresh/deployment constraints; check the current platform documentation.

A programme built around your team.

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