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MASTERY CHECKLIST · POWER BI

Power BI Mastery Checklist

The complete Power BI journey for aspiring data analysts. Phase 1 (0–2 yrs) and Phase 2 (2–3+ yrs) topics, 20 priority-ranked topics, a 30-60-90 day plan, 7 portfolio projects, real-world scenarios, and interview prep.

Beginner → Senior ~250 hrs totalIndustry-focused · Interview-ready
Progress0 / 20
0
Done
20
Topics
8
Phase 1
12
Phase 2
2
Easy
9
Medium
9
Hard

Priority-Based Topic Checklist

Organized by Phase (1 and 2). Expand each for context; check off as you master them.

  • 01.Power BI Fundamentals — Interface, Navigation & Core Concepts

    Most Critical
    EasyPhase 1 2–3 weeksOrder #1● Critical

  • 02.Power Query — Data Transformation & ETL

    Most Critical
    MediumPhase 1 3–4 weeksOrder #2● Critical

  • 03.Data Modeling — Relationships, Star Schema & DAX Basics

    Must Know
    MediumPhase 1 3–4 weeksOrder #3● Critical

  • 04.DAX Fundamentals — CALCULATE, FILTER, SUMX, COUNTROWS

    Most Critical
    MediumPhase 1 4–5 weeksOrder #4● Critical

  • 05.Visualization Basics — Charts, Formatting & Best Practices

    Must Know
    EasyPhase 1 2–3 weeksOrder #5● Critical

  • 06.Report Design — Layout, Navigation & User Experience

    Must Know
    MediumPhase 1 2–3 weeksOrder #6● High

  • 07.Filter Context & Row Context — DAX Execution

    Must Know
    HardPhase 1 3–4 weeksOrder #7● Critical

  • 08.Time Intelligence — YTD, MTD, QTD, Previous Periods

    Must Know
    HardPhase 1 4–5 weeksOrder #8● Critical

  • 09.Advanced DAX — Variables, Table Functions, Iterators

    Must Know
    HardPhase 2 4–5 weeksOrder #9● Critical

  • 10.Dashboard Design — Executive Dashboards, KPIs & Storytelling

    Must Know
    MediumPhase 2 3–4 weeksOrder #10● High

  • 11.Row-Level Security — User-Based Data Filtering

    Must Know Senior Level
    HardPhase 2 2–3 weeksOrder #11● High

  • 12.Performance Optimization — DAX Studio, VertiPaq Analyzer

    Senior Level
    HardPhase 2 3–4 weeksOrder #12● High

  • 13.Power BI Service — Workspaces, Sharing, Deployment

    Must Know
    MediumPhase 2 3–4 weeksOrder #13● Critical

  • 14.Data Integration — SQL, Excel, Python, APIs in Power BI

    Must Know
    MediumPhase 2 3–4 weeksOrder #14● High

  • 15.Paginated Reports — SSRS Style Reporting in Power BI

    HardPhase 2 2–3 weeksOrder #15● Medium

  • 16.Power BI + Python — Advanced Analytics & Visuals

    Senior Level
    HardPhase 2 3–4 weeksOrder #16● High

  • 17.Power BI + Excel — Bridge Between Tools

    Must Know
    MediumPhase 2 2–3 weeksOrder #17● High

  • 18.Power BI Embedded — Custom Applications

    Senior Level
    HardPhase 2 3–4 weeksOrder #18● Medium

  • 19.Power BI REST API — Automation & Governance

    HardPhase 2 2–3 weeksOrder #19● High

  • 20.Dataflows — Cloud-Based ETL in Power BI Service

    MediumPhase 2 2–3 weeksOrder #20● Medium
Phase 1 · 0–2 Years

Must Know for Early Data Analysts

Core Power BI skills every data analyst must master in their first two years. Tested in interviews and used daily.

Power BI Fundamentals

  • ›Understanding Power BI ecosystem: Desktop, Service, Mobile
  • ›Connecting to data sources: Excel, CSV, SQL, Web
  • ›Power BI interface: Visualizations, Fields, Filters panes
  • ›Creating your first report from scratch
  • ›Publishing and sharing reports
  • ›Understanding workspaces and apps
  • ›Power BI vs Power BI Pro vs Premium
  • ›Installation and setup best practices

Power Query — ETL Mastery

  • ›Connecting to multiple data sources
  • ›Appending and merging queries
  • ›Data cleaning: Remove duplicates, filter rows
  • ›Data transformation: Split columns, pivot, unpivot
  • ›M language basics — understanding the formula
  • ›Parameterized queries for dynamic reporting
  • ›Query folding — push transformations to source
  • ›Creating reusable functions and templates

Data Modeling Fundamentals

  • ›Star schema design: Fact and Dimension tables
  • ›Understanding relationships: 1:* and *:*
  • ›Relationship directions and cross-filtering
  • ›Creating calculated columns and measures
  • ›Active vs Inactive relationships with USERELATIONSHIP
  • ›Date table creation and marking as date table
  • ›Understanding cardinality and filtering
  • ›Data model normalization best practices

DAX Fundamentals

  • ›CALCULATE — The most important DAX function
  • ›FILTER — Row-level filtering in CALCULATE
  • ›SUMX, AVERAGEX — Iterator functions
  • ›COUNTROWS, DISTINCTCOUNT — Counting patterns
  • ›RELATED — Look up values from related tables
  • ›Basic time intelligence: TOTALYTD, TOTALMTD
  • ›Filter context vs Row context explained
  • ›Using variables (VAR) for cleaner code

Visualization Basics

  • ›Choosing the right chart for your data
  • ›Bar, Line, Pie, Scatter — when to use each
  • ›Conditional formatting for data-driven styling
  • ›Adding slicers and filters for interactivity
  • ›Using tooltips for detailed information
  • ›Visualization formatting best practices
  • ›Custom visuals from AppSource
  • ›Drill-through and drill-down functionality

Report Design & UX

  • ›Creating a consistent theme and color palette
  • ›Effective report navigation and bookmarks
  • ›Layout best practices for executive dashboards
  • ›Using buttons and shapes for interactivity
  • ›Designing for mobile and desktop views
  • ›Accessibility considerations in report design
  • ›Storytelling with data in Power BI
  • ›Performance considerations in report design
Phase 2 · 2–3+ Years

Advanced Power BI Skills for Senior Analysts

Advanced DAX, RLS, Performance, Service, Python integration, and enterprise capabilities.

Advanced DAX Patterns

  • ›Dynamic measure selection with Parameter tables
  • ›Ranking and Top N analysis with RANKX
  • ›Running totals and cumulative calculations
  • ›Period-over-period comparisons: MoM, YoY, QoQ
  • ›Parent-child hierarchies in DAX
  • ›Using SWITCH for complex logical measures
  • ›Advanced time intelligence with DATESINPERIOD
  • ›Creating dynamic aggregations with CALCULATE

Executive Dashboards

  • ›Executive dashboard design principles
  • ›Creating KPI cards and scorecards
  • ›Decomposition trees for root cause analysis
  • ›Smart narratives and AI visuals
  • ›Key Influencers visual for driver analysis
  • ›Building operational dashboards for teams
  • ›Strategic vs Tactical vs Operational dashboards
  • ›Getting feedback and iterating on dashboards

Row-Level Security (RLS)

  • ›Understanding RLS concepts and use cases
  • ›Creating dynamic RLS rules with DAX
  • ›Static RLS using roles and usernames
  • ›Testing RLS in Power BI Desktop
  • ›Managing RLS in Power BI Service
  • ›RLS with Active Directory groups
  • ›RLS for multi-tenant applications
  • ›Performance implications of RLS

Performance Optimization

  • ›Using DAX Studio for query performance analysis
  • ›VertiPaq Analyzer for data model optimization
  • ›Understanding storage engine and formula engine
  • ›Optimizing DAX measures for speed
  • ›Data reduction techniques: summarize, filter
  • ›Understanding cardinality and its impact
  • ›Performance best practices for large datasets
  • ›Monitoring performance in Power BI Service

Power BI Service & Governance

  • ›Workspace creation and management
  • ›Creating and managing data pipelines
  • ›Setting up scheduled data refresh
  • ›Gateway configuration for on-premises data
  • ›Power BI deployment pipelines
  • ›Managing permissions and sharing
  • ›Audit logs and governance best practices
  • ›Understanding Power BI Premium capacity

Data Integration & Sources

  • ›Connecting to SQL Server, Azure SQL, PostgreSQL
  • ›Using DirectQuery vs Import mode
  • ›Composite models: mixing DirectQuery and Import
  • ›Python in Power BI for advanced analytics
  • ›SQL queries in Power Query for complex data
  • ›Connecting to APIs and web data sources
  • ›Using Common Data Model and Dataverse
  • ›Managing data source credentials securely

Power BI + Advanced Analytics

  • ›Python visuals for custom charts and analysis
  • ›Using R scripts in Power BI for statistics
  • ›AI visuals: Key Influencers, Decomposition Tree
  • ›Integration with Azure Cognitive Services
  • ›Advanced forecasting with Python
  • ›Anomaly detection in Power BI
  • ›Machine learning integration with Azure ML
  • ›Sentiment analysis in Power BI with Python
Structured

30-60-90 Day Power BI Learning Plan

For a working analyst putting in 2–3 hrs/day. Phase 1 in Days 1–60. Phase 2 in Days 61–90 and beyond.

Days 1–30

Phase 1 — Foundation

Power BI Basics, Power Query, Data Modeling, DAX Fundamentals

Days 1–5
Power BI Fundamentals
Interface, connecting to data, creating first report, understanding visuals
Days 6–10
Power Query — Data Transformation
Connect, clean, transform, append, merge, query folding basics
Days 11–15
Data Modeling — Star Schema, Relationships
Fact and dimension tables, relationships, calculated columns
Days 16–20
DAX Fundamentals
CALCULATE, FILTER, SUMX, COUNTROWS, basic time intelligence
Days 21–25
Visualization & Report Design
Charts, formatting, slicers, bookmarks, navigation
Days 26–30
Weekend Project
Build a sales dashboard: ETL → Model → DAX → Visuals
Days 31–60

Phase 1 Complete — Advanced DAX & Design

Advanced DAX, Time Intelligence, Dashboard Design, Performance

Days 31–36
Advanced DAX — Time Intelligence
YTD, MTD, QTD, YoY, MoM, QoQ comparisons, PARALLELPERIOD
Days 37–42
Advanced DAX — Patterns
RANKX, TOPN, dynamic measures, parameter tables
Days 43–48
Executive Dashboard Design
KPI cards, scorecards, decomposition tree, storytelling
Days 49–54
Performance Optimization
DAX Studio, VertiPaq Analyzer, optimization techniques
Days 55–60
Project 2
Build an executive dashboard with advanced DAX measures
Days 61–90

Phase 2 — Enterprise & Advanced

Power BI Service, RLS, Integration, Python, Advanced Analytics

Days 61–66
Power BI Service
Workspaces, publishing, refresh scheduling, gateway configuration
Days 67–72
Row-Level Security (RLS)
Dynamic RLS, static RLS, testing, governance
Days 73–78
Data Integration
SQL, Python, Excel, APIs, DirectQuery, composite models
Days 79–84
Power BI + Python
Python visuals, advanced analytics, forecasting
Days 85–90
Capstone Project
End-to-end enterprise dashboard: ETL → Model → RLS → Service
Interview Prep

4-Phase Interview Preparation Roadmap

Expect live DAX exercises and data modeling scenarios — practice for speed and accuracy.

1

Foundation — Core Concepts Cold

Week 1 · Daily 1.5 hrs
Must explain without hesitation
  • ›Power Query — what it is and why it matters
  • ›Star schema vs Snowflake schema design
  • ›CALCULATE — how it changes filter context
  • ›Difference between calculated columns and measures
  • ›Import vs DirectQuery — when to use each
Practice method
  • ›Build a simple report from scratch in 30 minutes
  • ›Create a date table and basic time intelligence
  • ›Explain filter context to a non-technical person
  • ›Practice DAX patterns without looking up syntax
2

DAX & Data Modeling Sprint

Week 2 · Daily 2 hrs
Master these patterns
  • ›CALCULATE with multiple filters and ALL
  • ›Time intelligence: YTD, MTD, vs previous period
  • ›Create dynamic measures with parameter tables
  • ›Handle many-to-many relationships properly
  • ›Use variables for better DAX performance
Interview tips
  • ›Explain the difference between filter and row context
  • ›Discuss when to use iterator functions (SUMX, AVERAGEX)
  • ›Mention performance implications of DAX measures
  • ›Always consider the data model before writing DAX
3

Power Query & ETL

Week 3 · Daily 1.5 hrs
Real-world ETL scenarios
  • ›Combine multiple Excel files with different schemas
  • ›Handle slowly changing dimensions (SCD Type 1 and 2)
  • ›Parametrize your queries for dynamic refresh
  • ›Use query folding for performance optimization
  • ›Create reusable functions for common transformations
Standout elements
  • ›Show proficiency in M language queries
  • ›Demonstrate understanding of query folding
  • ›Handle errors and edge cases in data transformation
  • ›Document and organize your queries well
4

Service, Deployment & Advanced

Week 4 · 4–6 hrs per task
Enterprise-level skills
  • ›Set up a scheduled refresh and manage credentials
  • ›Implement Row-Level Security (RLS) with dynamic rules
  • ›Deploy reports using Power BI deployment pipelines
  • ›Optimize performance for a large dataset
  • ›Create a paginated report with Power BI Report Builder
Portfolio talking points
  • ›Real dashboard that saved stakeholders hours
  • ›Quantify impact: "10-page manual report → 1-click dashboard"
  • ›Mention handling of large datasets
  • ›Showcase complex DAX measures you built

Top Power BI Interview Questions

Common questions asked at data analyst interviews across tech, finance, and consulting.

Power Query & Data Preparation

  1. What is Power Query and how does it differ from traditional Excel?
  2. Explain query folding and why it matters for performance.
  3. How would you combine data from 12 monthly Excel files with different headers?
  4. What is the M language and when would you write custom M code?
  5. How do you handle slowly changing dimensions in Power Query?
  6. What is the difference between appending and merging queries?

Data Modeling

  1. What is a star schema and why is it preferred in Power BI?
  2. Explain the difference between calculated columns and measures.
  3. What is filter context and how does it affect DAX calculations?
  4. How do you create a date table and why is it important?
  5. What are bidirectional relationships and when should you use them?
  6. Explain row context vs filter context with examples.

DAX Fundamentals

  1. What is CALCULATE and why is it the most important DAX function?
  2. Explain the difference between SUM and SUMX with examples.
  3. How would you calculate year-over-year growth in DAX?
  4. What is the difference between CALCULATE and FILTER?
  5. How do you create a running total in DAX?
  6. What are variables in DAX and why use them?

Visualization & Design

  1. How do you choose the right chart type for your data?
  2. What are your best practices for dashboard design?
  3. How would you create an executive dashboard with 5 KPIs?
  4. Explain bookmarks and how they improve user experience.
  5. What is drill-through functionality and how do you implement it?
  6. How do you design for both desktop and mobile viewing?

Service & Deployment

  1. How do you set up scheduled refresh for a Power BI dataset?
  2. What is Row-Level Security and how do you implement it?
  3. Explain the difference between workspaces and apps.
  4. How do you use deployment pipelines for CI/CD?
  5. What is a gateway and when is it needed?
  6. How do you monitor and optimize Power BI Service performance?
Portfolio

Hands-On Projects & Portfolio Ideas

Build these to prove real-world Power BI mastery. Each covers multiple topic areas.

P1Beginner 4–6hrs

Sales Performance Dashboard

Connect to sales data, clean with Power Query, build star schema, create 10+ DAX measures, design interactive dashboard with slicers and drill-through.

P2Beginner 5–7hrs

Financial Reporting Dashboard

Import financial data, create P&L statements with DAX, build budget vs actual comparison, create variance analysis with conditional formatting.

P3Intermediate 6–8hrs

HR Analytics Dashboard

Connect to employee data, create headcount and attrition measures, build retention cohort analysis, design executive HR dashboard.

P4Intermediate 8–10hrs

Customer 360 Dashboard

Integrate data from multiple sources (CRM, sales, support), build 360-degree customer view with RFM segmentation, create customer journey visuals.

P5Advanced 10–12hrs

Supply Chain Analytics Dashboard

Connect to supply chain data sources, build inventory and order analytics, create supplier performance dashboard, implement RLS for supplier access.

P6Advanced 12–15hrs

Enterprise Executive Dashboard

Build comprehensive executive dashboard with 20+ KPIs, implement dynamic measures, optimize for performance, deploy to Power BI Service with RLS.

P7Advanced 15–20hrs

Power BI + Python Advanced Analytics

Integrate Python for predictive analytics in Power BI, build forecasting models, create custom visuals with matplotlib, deploy as paginated reports.

Real-World

Business Scenarios You'll Actually Face

Common asks from actual analyst roles. Know how to solve each end-to-end.

📊Scenario 1 — 'Executive needs a weekly sales dashboard'

Requirements
  • ›Weekly sales by region and product
  • ›YTD vs Budget comparison
  • ›Current month performance
  • ›Interactive filters by region
  • ›Mobile-friendly design
Power BI solution
  • ›Connect to SQL sales database
  • ›Power Query for data transformation
  • ›Star schema with Date, Product, Region dims
  • ›DAX measures for KPIs and time intelligence
  • ›Bookmarks for executive navigation
Outcome
  • ›Single source of truth for sales
  • ›30-min manual report → 1-click refresh
  • ›Executive decision-making improved
  • ›Version controlled with deployment pipelines

🔒Scenario 2 — 'Sales managers should only see their region'

Challenge
  • ›10+ regional sales managers
  • ›Each should see only their region's data
  • ›Executive should see all data
  • ›Data security is critical
Power BI solution
  • ›Implement Row-Level Security (RLS)
  • ›Dynamic rules based on user email
  • ›Use USERPRINCIPALNAME() in DAX
  • ›Create roles for each region
  • ›Test RLS with 'View as Role' feature
Outcome
  • ›Data security without duplication
  • ›Single report for all users
  • ›Centralized management
  • ›Audit-ready security controls

🚀Scenario 3 — '20M row dataset is too slow'

Symptom
  • ›Report takes 45 seconds to load
  • ›Visuals are slow to respond
  • ›Users are complaining
  • ›Data grows 1M rows per month
Optimization
  • ›Analyze with DAX Studio and VertiPaq
  • ›Reduce cardinality in key columns
  • ›Aggregate data where possible
  • ›Use summarized tables for dashboard
  • ›Implement incremental refresh
Results
  • ›Load time: 45s → 3s
  • ›Visuals respond instantly
  • ›Users happy with performance
  • ›Pattern reusable for other datasets

🔄Scenario 4 — 'Monthly reports are taking 4 days to prepare'

Current process
  • ›Data pulled from 5 systems
  • ›Manual Excel consolidation
  • ›Pivot tables and charts manually
  • ›Report distributed via email
  • ›Takes 4 days each month
Power BI solution
  • ›Connect directly to all data sources
  • ›Power Query for ETL pipeline
  • ›DAX for all calculations
  • ›Automated refresh with gateway
  • ›Publish to Power BI Service
Impact
  • ›4-day manual process → 1-hour review
  • ›100% reduction in errors
  • ›Real-time data availability
  • ›Stakeholders self-serve insights
Standards

Power BI Best Practices Used at Companies

Standards followed by professional analysts at product companies, consulting firms, and data-driven enterprises.

Data Modeling

  • ›Always use star schema — fact and dimension tables
  • ›Create a separate date table for time intelligence
  • ›Avoid bidirectional relationships when possible
  • ›Use calculated columns sparingly — prefer measures
  • ›Normalize dimensions to reduce redundancy
  • ›Use surrogate keys for all dimension tables

DAX Performance

  • ›Use variables in DAX to reduce recalculation
  • ›Avoid using FILTER inside CALCULATE when a simple filter works
  • ›Use iterators (SUMX) only when necessary
  • ›Pre-calculate in Power Query when possible
  • ›Use DIVIDE instead of / to handle divide by zero
  • ›Avoid using ALL in measures when not needed

Visualization

  • ›Choose the right chart for your data type
  • ›Maintain consistent color palette across reports
  • ›Use conditional formatting to highlight outliers
  • ›Add clear titles and labels to all visuals
  • ›Use tooltips for additional context
  • ›Keep it simple — less is more

Power Query

  • ›Always use query folding when possible
  • ›Remove unnecessary columns at source
  • ›Filter rows as early as possible
  • ›Create reusable functions for common transformations
  • ›Parameterize your queries for flexibility
  • ›Document all transformation steps clearly

Service & Governance

  • ›Set up scheduled refresh with proper credentials
  • ›Use deployment pipelines for development → test → production
  • ›Implement RLS for data security
  • ›Monitor usage and performance metrics
  • ›Set up data alerts for important KPIs
  • ›Create and maintain data dictionaries

Code Organization

  • ›Organize DAX measures into folders in the model
  • ›Use consistent naming conventions for measures
  • ›Comment complex DAX formulas
  • ›Group related queries in Power Query
  • ›Maintain version control for .pbix files
  • ›Document your data model design decisions
Level Up

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