Power BI Complete Roadmap

Course Objective:

• Build a complete understanding of Microsoft Power BI from data fundamentals to professional-level business intelligence and analytics.
• Learn Power BI Desktop, Power BI Service, Power Query, data modeling, DAX, visualization, dashboards, reports, and deployment.
• Develop practical skills with data transformation, star-schema modeling, relationships, calculated columns, measures, and advanced DAX.
• Explore Power BI Service administration, workspaces, security, gateways, refresh, deployment pipelines, performance optimization, and governance.
• Prepare for real-world Power BI projects, portfolio development, interviews, and role-aligned Microsoft certification.

1

Data Analytics & Business Intelligence Fundamentals

• What is data analytics?
• Business intelligence concepts
• Descriptive, diagnostic, predictive, and prescriptive analytics
• Structured vs unstructured data
• KPIs and business metrics
• Dimensions and measures
• Data-driven decision making
• Reporting vs dashboards
• BI project lifecycle
• Introduction to Microsoft Power BI

2

Power BI Ecosystem & User Interface

• Power BI Desktop
• Power BI Service
• Power BI Mobile
• Power BI Report Server overview
• Power BI workspace concepts
• Report, dashboard, semantic model, and app
• Power BI interface and navigation
• Import, DirectQuery, and live connection overview
• File formats and PBIX basics
• Power BI licensing concepts

3

Data Sources & Connectivity

• Excel and CSV files
• Text and folder sources
• SQL Server and relational databases
• SharePoint and OneDrive
• Web and REST-based sources
• Power BI data sources overview
• Dataverse overview
• Connecting to multiple data sources
• Import vs DirectQuery
• Choosing the appropriate connectivity mode

4

Power Query Fundamentals

• Power Query Editor
• Query steps and applied steps
• Data types
• Filtering and sorting
• Removing duplicates and errors
• Replacing values
• Splitting and merging columns
• Renaming and reordering columns
• Fill and conditional transformations
• Managing queries and groups

5

Advanced Power Query & M

• M language fundamentals
• Custom columns
• Conditional columns
• Custom functions
• Parameters
• Merge queries
• Append queries
• Reference vs duplicate queries
• Query folding concepts
• Reusable transformation patterns

6

Data Cleaning & Transformation

• Handling missing values
• Error handling
• Data standardization
• Text transformations
• Date and time transformations
• Number transformations
• Unpivot and pivot
• Transpose and group by
• Data profiling
• Building repeatable ETL workflows

7

Data Modeling Fundamentals

• What is a semantic model?
• Tables, fields, and hierarchies
• Relationships
• Primary and foreign keys
• Cardinality
• Cross-filter direction
• Active and inactive relationships
• Star schema
• Fact and dimension tables
• Modeling best practices

8

Advanced Data Modeling

• Snowflake vs star schema
• Role-playing dimensions
• Bridge tables
• Many-to-many relationships
• Degenerate dimensions
• Date tables
• Disconnected tables
• Calculation groups overview
• Field parameters
• Model documentation and organization

9

Power BI Visualizations

• Bar and column charts
• Line and area charts
• Pie and donut charts
• Tables and matrices
• Cards and KPI visuals
• Slicers
• Maps and geographic visuals
• Treemaps and decomposition trees
• Waterfall and funnel charts
• Choosing the right visual

10

Report Design & User Experience

• Report canvas and page layouts
• Themes
• Formatting standards
• Visual interactions
• Drill-down
• Drill-through
• Tooltips
• Bookmarks
• Buttons and navigation
• Accessibility and report usability

11

DAX Fundamentals

• What is DAX?
• Calculated columns
• Measures
• DAX syntax
• Variables
• Basic aggregations
• SUM, AVERAGE, MIN, MAX, COUNT
• DISTINCTCOUNT
• IF and SWITCH
• CALCULATE fundamentals

12

Intermediate DAX

• Filter context
• Row context
• Context transition
• CALCULATE and filter expressions
• ALL and REMOVEFILTERS
• VALUES and SELECTEDVALUE
• FILTER
• RELATED and RELATEDTABLE
• DIVIDE
• Iterator functions such as SUMX and AVERAGEX

13

Advanced DAX & Time Intelligence

• Date intelligence concepts
• TOTALYTD, TOTALMTD, and TOTALQTD
• DATEADD
• SAMEPERIODLASTYEAR
• Running totals
• Year-over-year analysis
• Month-over-month analysis
• Rolling averages
• Dynamic measures
• Advanced filter context and virtual tables

14

Advanced Analytics with DAX

• Virtual tables
• ADDCOLUMNS
• SUMMARIZE and SUMMARIZECOLUMNS
• CALCULATETABLE
• TREATAS
• Variables and reusable logic
• Ranking with RANKX
• Dynamic segmentation
• Dynamic titles and labels
• Advanced KPI patterns

15

Power BI Service

• Publishing reports
• Workspaces
• Semantic models
• Dashboards
• Apps
• Sharing and collaboration
• Workspace roles
• Subscriptions
• Alerts
• Comments and collaboration workflows

16

Data Refresh, Gateways & Deployment

• Scheduled refresh
• On-demand refresh
• On-premises data gateway
• Gateway configuration concepts
• Data source credentials
• Incremental refresh
• Deployment pipelines
• Development, test, and production environments
• Release management
• Deployment best practices

17

Power BI Security & Governance

• Row-level security (RLS)
• Static and dynamic RLS
• USERPRINCIPALNAME and identity functions
• Object-level security overview
• Workspace permissions
• Sharing controls
• Sensitivity labels
• Microsoft Purview overview
• Governance policies
• Secure distribution of reports

18

Power BI Performance Optimization

• Performance Analyzer
• Reducing model size
• Choosing efficient data types
• Star schema optimization
• DAX optimization
• Reducing visual complexity
• Query folding
• Aggregations
• Import vs DirectQuery performance
• Troubleshooting slow reports

19

Advanced Connectivity & Composite Models

• Composite models
• Dual storage mode
• DirectQuery
• Live connections
• Direct Lake overview
• Aggregations
• Cross-source modeling
• Semantic model connectivity
• XMLA endpoint overview
• Enterprise-scale connectivity concepts

20

Power BI with SQL & Data Platforms

• SQL fundamentals for Power BI
• SELECT and filtering
• JOINs
• GROUP BY and aggregations
• Views and stored procedures
• SQL-based data preparation
• Power BI SQL overview
• Data warehouse concepts
• Lakehouse concepts
• Connecting enterprise data platforms to Power BI

21

Power BI and Microsoft Fabric

• Microsoft Fabric overview
• OneLake concepts
• Lakehouses
• Warehouses
• Data Factory in Fabric
• Dataflows Gen2
• Notebooks overview
• Semantic models in Fabric
• Direct Lake concepts
• End-to-end analytics architecture

22

Power BI Automation & Integration

• Power Automate integration
• Power Apps integration
• Power BI REST API overview
• Embedding concepts
• Exporting reports
• Subscriptions and automation
• Power BI service automation
• Deployment automation concepts
• External tools overview
• Enterprise integration scenarios

23

Hands-on Projects & Portfolio

• Sales dashboard project
• Finance and profitability dashboard
• HR analytics dashboard
• Marketing performance report
• Inventory and operations dashboard
• Customer analytics project
• Executive KPI dashboard
• End-to-end ETL and semantic model project
• RLS and deployment project
• Document projects with business requirements, model, DAX, and insights

24

Certification & Interview Preparation

• Microsoft Power BI certification path overview
• PL-300 skill areas
• Power Query interview questions
• Data modeling interview questions
• DAX interview questions
• Visualization and UX scenarios
• Power BI Service scenarios
• RLS and security scenarios
• Performance troubleshooting questions
• Project and portfolio walkthrough preparation

25

Professional-Level Power BI Specialization

• Choose a primary Power BI career track
• Power BI Developer
• BI Analyst
• Data Analyst
• Power BI Consultant
• BI Engineer
• Fabric Analytics Engineer
• Enterprise Power BI architecture
• Governance and Center of Excellence concepts
• Production-scale analytics and stakeholder management

Skills You'll Gain:

• Strong understanding of business intelligence, data analytics, and the Power BI ecosystem.
• Ability to connect, clean, transform, and combine data using Power Query and M.
• Practical data modeling skills including star schemas, relationships, dimensions, facts, and semantic models.
• Ability to write beginner-to-advanced DAX measures for KPIs, time intelligence, and analytical calculations.
• Experience designing interactive, accessible, and business-focused Power BI reports and dashboards.
• Practical knowledge of Power BI Service, workspaces, sharing, refresh, gateways, deployment, and security.
• Ability to optimize Power BI models and reports for performance and enterprise-scale usage.
• Experience building portfolio projects suitable for technical discussions, interviews, and professional work.

Duration:

Typically 3 to 6 months for a strong Power BI foundation and job-ready portfolio. Reaching professional-level capability depends on your prior data experience, DAX depth, project complexity, and practice time.

Certification:

Microsoft Certified: Power BI Data Analyst (PL-300) is a common role-aligned certification path. Microsoft can change exam objectives and requirements, so verify the current official exam scope and availability before registering. Certification should complement hands-on Power BI practice.