Advanced Power BI

Advanced Power BI

Intelligent Analytics, Modeling & Automation

From Reports to Decision Systems

After mastering basic report creation and visual exploration, the real value of Power BI emerges when it is treated as a data analytics and decision platform, not just a reporting tool. Modern Power BI solutions demand strong data models, well-written DAX, time-aware analysis, automation, and increasing use of AI-assisted features.

This advanced two-day course is designed for professionals who already know how to build reports and now need to design analytical solutions that scale, refresh automatically, integrate multiple data sources, and support real business decisions. The course is led by an instructor with over 30 years of industry experience, focusing on industry-demanded analytics practices rather than academic theory.

Learning Outcomes

By the end of this course, participants will be able to:

• Design efficient and scalable Power BI data models
• Write foundational DAX measures for business analytics
• Perform time series analysis, trend evaluation, and forecasting
• Build highly interactive reports using advanced visuals and interactions
• Integrate Power BI with multiple data sources and automated refresh pipelines
• Apply AI-driven features such as Copilot and built-in AI visuals
• Use Python within Power BI for advanced analytics and visualization

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

Training Outline (2-Day Course)

1. Power BI as an Analytics Platform

• Reframing Power BI beyond static reporting
• Semantic models and enterprise analytics thinking
• Import vs DirectQuery vs composite models
• Common mistakes made after beginner-level Power BI usage
• Designing reports for decision-making, not decoration

2. Data Modeling Fundamentals

• 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

3. DAX Fundamentals for Business Analytics

• 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

4. Time Series Analysis & 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

5. Advanced Visual Interactivity

• 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

6. Custom Visuals and Extended Visualization

• 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

7. Automated Dashboards & Data Integration

• Integrating Power BI with Dataverse, SQL, and cloud data sources
• Scheduled refresh, incremental refresh, and dataflows
• Designing dashboards for near-real-time analytics
• Power BI service architecture and workspace strategies
• Sharing, app publishing, and controlled access

8. AI Capabilities in Power BI

• Overview of AI features available in Power BI
• Copilot-assisted report and DAX generation
• AI visuals for anomaly detection and key influencers
• Natural language Q&A and AI-generated insights
• Responsible and governed use of AI features

9. Python Integration in Power BI

• When and why to use Python in Power BI
• Python scripts in Power Query for data transformation
• Python visuals for advanced analytics and custom charts
• Using Python for statistical analysis and forecasting
• Performance and deployment considerations