Master Power BI & Become a Job-Ready Data Professional
Power BI Training
At Boweyon Tech, our Power BI program is built to transform beginners into confident data analysts. Learn how to clean, model, visualize, and present real business data using industry-standard tools and real-time projects.
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About the Program
Transform Data into Business Intelligence
Power BI is one of the most in-demand data visualization tools used by organizations worldwide to make smarter decisions. At Boweyon Tech, our Power BI training program is designed to help learners move beyond theory and develop real analytical skills used in modern companies.
Through structured training, practical dashboards, and real-world datasets, students learn how to collect, transform, analyze, and visualize data effectively. This program helps learners build a strong portfolio and prepares them for roles in data analytics and business intelligence.
Why Choose Boweyon PowerBi Training
At Boweyon Tech, our Power BI training is designed to help learners move beyond basic concepts and develop practical data analysis skills. The program focuses on real-world dashboards, structured learning, and building the expertise required for modern data-driven roles.
What You Will Learn in Our Power BI Training
Our Power BI training at Boweyon Tech is designed to help learners build strong data analysis and visualization skills used in modern businesses. The program focuses on practical learning, enabling students to work with real datasets and develop professional dashboards.
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Placements
From Learning to Earning — Proven Placement Outcomes
Our Success
Not Just Training. Measurable Career Results.
At Boweyon Tech, we focus on making every learner job-ready no matter their background. Our structured training, real-time projects, and interview support help students confidently enter the data analytics field.
01
🎓 Freshers
Start your career in data analytics with strong fundamentals, portfolio, and interview preparation.
02
💼 Experienced
Transition into high-demand data roles by upgrading your skills with Power BI, DAX, and business analytics.
03
Career Gap
Re-enter the workforce with confidence through structured learning, practical exposure, and support.
Syllabus
Power BI Training Syllabus – From Data to Decision Making
• Business Intelligence fundamentals
• OLTP vs OLAP
• Reporting vs Analytics vs Dashboards
• Power BI ecosystem overview
• Power BI Desktop, Service, Mobile
• Dataset vs Report vs Dashboard
• Import vs DirectQuery vs Live Connection
• Enterprise BI architecture
• Power BI in Microsoft Fabric
• Where Power BI fits in Microsoft Fabric
• OLTP vs OLAP
• Reporting vs Analytics vs Dashboards
• Power BI ecosystem overview
• Power BI Desktop, Service, Mobile
• Dataset vs Report vs Dashboard
• Import vs DirectQuery vs Live Connection
• Enterprise BI architecture
• Power BI in Microsoft Fabric
• Where Power BI fits in Microsoft Fabric
• Power BI Desktop installation (64-bit only)
• Interface: Report, Data, Model views
• File formats: PBIX, PBIT, PBIR
• Options & global settings
• Auto Date/Time behavior
• Development workflow best practices
• First dataset load & save
• Interface: Report, Data, Model views
• File formats: PBIX, PBIT, PBIR
• Options & global settings
• Auto Date/Time behavior
• Development workflow best practices
• First dataset load & save
• Excel, CSV, Folder sources
• SQL Server, Azure SQL
• SharePoint, OneDrive
• Web & REST APIs
• Authentication methods
• Import vs DirectQuery behavior
• Source performance considerations
• SQL Server, Azure SQL
• SharePoint, OneDrive
• Web & REST APIs
• Authentication methods
• Import vs DirectQuery behavior
• Source performance considerations
• Power Query Editor interface
• ETL lifecycle (Extract, Transform, Load)
Data Profiling
• Column Quality
• Column Distribution
• Column Profile
Column Operations
• Rename columns
• Split columns
• Merge columns
• Extract values
• Replace values
Row Operations
• Filter rows
• Remove rows
• Keep rows
• Sort rows
• Append queries vs Merge queries
• Group By & aggregations
• Applied steps management
• Query folding concepts
• Transformation performance impact
Parameters
• What parameters are
• Why parameters are used
• Where parameters are applied
• ETL lifecycle (Extract, Transform, Load)
Data Profiling
• Column Quality
• Column Distribution
• Column Profile
Column Operations
• Rename columns
• Split columns
• Merge columns
• Extract values
• Replace values
Row Operations
• Filter rows
• Remove rows
• Keep rows
• Sort rows
• Append queries vs Merge queries
• Group By & aggregations
• Applied steps management
• Query folding concepts
• Transformation performance impact
Parameters
• What parameters are
• Why parameters are used
• Where parameters are applied
• Conditional columns
• Custom columns
• Index columns
• Pivot & Unpivot
• Error handling strategies
• M language fundamentals
• Reusable queries
• Template-based PBIX design
• API pagination handling
• Power Query performance tuning
• Custom columns
• Index columns
• Pivot & Unpivot
• Error handling strategies
• M language fundamentals
• Reusable queries
• Template-based PBIX design
• API pagination handling
• Power Query performance tuning
• What is a data model
• Fact vs Dimension tables
• Star schema design
• Snowflake schema design
• Relationships & cardinality
• Cross-filter direction
• Active vs inactive relationships
• Date dimension importance
• Model performance best practices
• Fact vs Dimension tables
• Star schema design
• Snowflake schema design
• Relationships & cardinality
• Cross-filter direction
• Active vs inactive relationships
• Date dimension importance
• Model performance best practices
• Surrogate keys
• Role-playing dimensions
• Bridge tables
• Many-to-many patterns
• Slowly Changing Dimensions (Type 1)
• Calculated columns vs calculated tables
• Composite models (Import & DirectQuery)
• Role-playing dimensions
• Bridge tables
• Many-to-many patterns
• Slowly Changing Dimensions (Type 1)
• Calculated columns vs calculated tables
• Composite models (Import & DirectQuery)
• Calculated columns vs measures
• Row context
• Filter context
• Evaluation context
• Aggregation functions
• Logical, text & math functions
• Iterator (X) functions
• CALCULATE introduction
• Basic time intelligence
• Row context
• Filter context
• Evaluation context
• Aggregation functions
• Logical, text & math functions
• Iterator (X) functions
• CALCULATE introduction
• Basic time intelligence
• CALCULATE deep dive
• Context transition
• FILTER vs ALL vs ALLEXCEPT
• REMOVEFILTERS
• USERELATIONSHIP
• CROSSFILTER
• Semi-additive measures
• Ranking & percentiles
• Moving averages
• Advanced iterator usage
• Context transition
• FILTER vs ALL vs ALLEXCEPT
• REMOVEFILTERS
• USERELATIONSHIP
• CROSSFILTER
• Semi-additive measures
• Ranking & percentiles
• Moving averages
• Advanced iterator usage
• FILTER table expressions
• SUMMARIZE
• ADDCOLUMNS
• SELECTCOLUMNS
• TOPN
• CROSSJOIN
• Virtual tables inside measures
• DAX debugging techniques
• SUMMARIZE
• ADDCOLUMNS
• SELECTCOLUMNS
• TOPN
• CROSSJOIN
• Virtual tables inside measures
• DAX debugging techniques
• Date table requirements
• YTD, QTD, MTD
• SAMEPERIODLASTYEAR
• DATEADD
• Parallel period analysis
• Fiscal calendar handling
• Custom time calculations
• YTD, QTD, MTD
• SAMEPERIODLASTYEAR
• DATEADD
• Parallel period analysis
• Fiscal calendar handling
• Custom time calculations
• Bar, Column, Line, Area charts - Core Visuals
• Tables & Matrix
• Cards & KPIs
• Slicers
• Filters pane
• Visual interactions
• Sorting & formatting
• Report layout fundamentals
• Tables & Matrix
• Cards & KPIs
• Slicers
• Filters pane
• Visual interactions
• Sorting & formatting
• Report layout fundamentals
• Drill-down
• Drill-through
• Tooltips
• Bookmarks
• Buttons & navigation
• Field parameters
• Small multiples
• Custom visuals (enterprise rules)
• UX best practices
• Drill-through
• Tooltips
• Bookmarks
• Buttons & navigation
• Field parameters
• Small multiples
• Custom visuals (enterprise rules)
• UX best practices
• DAX Performance Analyzer
• Visual performance tuning
• Model size reduction
• Cardinality control
• Star schema optimization
• VertiPaq fundamentals
• Storage engine vs Formula engine
• Performance troubleshooting checklist
• Visual performance tuning
• Model size reduction
• Cardinality control
• Star schema optimization
• VertiPaq fundamentals
• Storage engine vs Formula engine
• Performance troubleshooting checklist
• Row Level Security (static)
• Dynamic Row Level Security
• USERNAME & USERPRINCIPALNAME
• Object Level Security
• Security testing methodology
• Dynamic Row Level Security
• USERNAME & USERPRINCIPALNAME
• Object Level Security
• Security testing methodology
• Workspace strategy
• Apps vs direct sharing
• Dataset endorsement
• Thin report architecture
• Shared semantic models
• Scheduled refresh
• Versioning mindset
Licensing
• Free vs Pro vs PPU
• Premium vs Fabric capacity
• Creator vs consumer licensing
• Licensing impact on:
Workspaces
Apps
Row Level Security
Deployment pipelines
Dataflows
• Common licensing mistakes
• Apps vs direct sharing
• Dataset endorsement
• Thin report architecture
• Shared semantic models
• Scheduled refresh
• Versioning mindset
Licensing
• Free vs Pro vs PPU
• Premium vs Fabric capacity
• Creator vs consumer licensing
• Licensing impact on:
Workspaces
Apps
Row Level Security
Deployment pipelines
Dataflows
• Common licensing mistakes
• Gateway architecture
• Installation & configuration
• Data source mapping
• Credential handling
• Refresh failure troubleshooting
• Installation & configuration
• Data source mapping
• Credential handling
• Refresh failure troubleshooting
• Incremental refresh concepts
• RangeStart & RangeEnd
• Partition behavior
• Refresh testing
• Enterprise-scale use cases
• RangeStart & RangeEnd
• Partition behavior
• Refresh testing
• Enterprise-scale use cases
• Mobile layouts
• Responsive visuals
• Device-specific UX
• Testing strategies
• Responsive visuals
• Device-specific UX
• Testing strategies
• Dataflows Gen2
• Centralized ETL design
• Linked tables
• Parameterized dataflows
• Dataflows vs PBIX Power Query
• Performance & governance
• Enterprise reuse scenarios
• Centralized ETL design
• Linked tables
• Parameterized dataflows
• Dataflows vs PBIX Power Query
• Performance & governance
• Enterprise reuse scenarios
• Dev / Test / Prod strategy
• Deployment pipelines
• Git integration overview
• PBIX versioning limitations
• Naming & branching strategy
• Release & rollback practices
• Deployment pipelines
• Git integration overview
• PBIX versioning limitations
• Naming & branching strategy
• Release & rollback practices
• Streaming datasets
• Push datasets
• DirectQuery latency
• Near real-time vs true real-time
• Event-driven vs refresh-driven models
• When Power BI should not be used
• Push datasets
• DirectQuery latency
• Near real-time vs true real-time
• Event-driven vs refresh-driven models
• When Power BI should not be used
• Key Influencers visual
• Decomposition Tree visual
• Smart Narrative
• Forecasting (built-in analytics)
• Q&A visual (going to deprecated - legacy - not recommended for new builds)
• Production-safe vs demo-only AI features
• Decomposition Tree visual
• Smart Narrative
• Forecasting (built-in analytics)
• Q&A visual (going to deprecated - legacy - not recommended for new builds)
• Production-safe vs demo-only AI features
• Power BI with Excel
• Power BI with SharePoint
• Power BI with Power Apps
• Power Automate integration
• Dataverse integration
• Power BI with SharePoint
• Power BI with Power Apps
• Power Automate integration
• Dataverse integration
• Workspace governance
• Tenant settings
• Sensitivity labels
• Data lineage
• Compliance & audit readiness
• Tenant settings
• Sensitivity labels
• Data lineage
• Compliance & audit readiness
• Bi-directional relationship misuse
• Excessive calculated columns
• DISTINCTCOUNT traps
• Poor date table design
• Visual overloading
• Bad RLS patterns
• Dashboard rejection reasons
• Model refactoring techniques
• Excessive calculated columns
• DISTINCTCOUNT traps
• Poor date table design
• Visual overloading
• Bad RLS patterns
• Dashboard rejection reasons
• Model refactoring techniques
• Paginated reports concepts
• RDL overview
• Power BI Report Server
• On-premises reporting scenarios
• RDL overview
• Power BI Report Server
• On-premises reporting scenarios
• Sales analytics
• Finance performance
• HR analytics
• Inventory management
• Marketing analytics
• Customer churn
• Manufacturing OEE
Each project includes:
• Business problem statement
• KPIs and success metrics
• Data volume & refresh constraints
• Security model and access model
• Performance considerations
• Stakeholder personas
• Finance performance
• HR analytics
• Inventory management
• Marketing analytics
• Customer churn
• Manufacturing OEE
Each project includes:
• Business problem statement
• KPIs and success metrics
• Data volume & refresh constraints
• Security model and access model
• Performance considerations
• Stakeholder personas
Testimonials