2.1: Connecting to Different Data Sources and Choosing the Right Connector

2.2: Understanding ETL and the Difference Between Front-End Reporting and Back-End Data Prep

2.3: Cleaning, Shaping, and Standardizing Data Before Analysis

2.4: Working With Data Types, Locale-Based Type Changes, Text Transformations, Number Transformations, and Date-Time Transformations

2.5: Using Conditional Columns, Grouping, Merging, Appending, and Folder-Based Ingestion

2.6: Managing Parameters for Dynamic Data Sources

2.7: Refreshing Queries and Maintaining Repeatable Data Pipelines

2.8: Data Quality Checks, Profiling, and Source Validation

3.1: Database Normalization and Why Structured Models Improve Reporting Reliability

3.2: Fact and Dimension Tables in a Business Intelligence Model

3.3: Primary Keys, Foreign Keys, and Relationship Design

3.4: Star Schema and Snowflake Schema Design for Scalable Reporting

3.5: Active vs Inactive Relationships and Cardinality

3.6: Relationship Management and Model Governance

3.7: Multiple Fact Table Modelling

3.8: Field Hiding, Data Categorization, Hierarchy Design, and Model Usability

3.9: Expanded Tables, Context Transition, and Evaluation Order

4.1: When to Use Calculated Columns Versus Measures

4.2: Building Calculated Columns for Row-Level Business Logic

4.3: Text, Date, Logical, and Conditional Functions

4.4: Nested Functions for Business Rules

4.5: Variables for Readable and Maintainable Formulas

4.6: SWITCH for Classification and Business Mapping

4.7: Error Handling With IFERROR

4.8: Measures, Implicit vs Explicit Measures, and Dedicated Measure Tables

4.9: Filter Context, Evaluation Sequence, and Measure Behavior

4.10: Basic Math, Statistical Calculations, and RELATED

5.1: CALCULATE as the Core Engine of Business Logic

5.2: FILTER, ALL, REMOVEFILTERS, KEEPFILTERS, ALLEXCEPT, and ALLSELECTED

5.3: Iterator Patterns With X Functions

5.4: TOPN for Rank-Based Analysis

5.5: ISBLANK and COALESCE for Data Completion and Clean Reporting

5.6: Common Scalar, Rounding, Information, and Conversion Functions

5.7: Table Functions: DISTINCT, VALUES, SELECTEDVALUE, SELECTCOLUMNS, ADDCOLUMNS, SUMMARIZE

5.8: Table Construction: ROW, DATATABLE, GENERATESERIES

5.9: Combining and Comparing Datasets With CROSSJOIN, UNION, EXCEPT, and INTERSECT

5.10: Physical vs Virtual Relationships

5.11: RELATED, RELATEDTABLE, USERELATIONSHIP, and CROSSFILTER

5.12: Practical CALCULATE Patterns for Sales, Finance, and KPI Analysis

6.1: Building a Reusable Date Table

6.2: Date Table Requirements and Calendar Logic

6.3: CALENDAR and CALENDARAUTO

6.4: DATEADD and Period Shifting

6.5: SAMEPERIODLASTYEAR, PREVIOUSQUARTER, and PARALLELPERIOD

6.6: Week-Based and Fiscal Calendar Calculations

6.7: Fiscal Period-to-Date and Previous Fiscal Period Logic

6.8: Cumulative Analysis With MTD, QTD, and YTD Patterns

6.9: DATESINPERIOD and DATESBETWEEN for Rolling Windows

6.10: ENDOFMONTH and Period-End Analysis

7.1: Choosing the Right Visuals for the Business Question

7.2: Card, Multi-Row Card, KPI, Gauge, Line, Area, Bar, Donut, Table, and Matrix Visuals

7.3: Formatting Charts for Executive Readability

7.4: Trend Lines and Forecasts

7.5: Filtering, Slicers, and Top-N Logic

7.6: Conditional Formatting for Business Exceptions

7.7: Drill Down, Drill Up, and Drillthrough Analysis

7.8: Report Interactions and Navigation Control

7.9: Bookmarks, Custom Navigation Buttons, and Guided Storytelling

7.10: Custom Tooltips, Numeric Range Filters, and Field Parameters

7.11: Map Visuals for Location-Based Reporting

7.24: PowerPoint and Figma based Report Designs

8.1: Anomaly Detection for Exception Spotting

8.2: Smart Narratives for Automated Insight Summaries

8.3: Q&A Visuals for Natural-Language Querying

8.4: Decomposition Trees for Root-Cause Analysis

8.5: Key Influencers for Driver Analysis

9.1: Apply the Three-Layer Storytelling Model to Transform Analysis into Compelling Reports

9.2: The Pyramid Principle: Structuring Your Insight for Executive Audiences

9.3: Annotations, Reference Lines, and Callouts: Guiding the Reader's Eye

9.4: Executive Summary Dashboards: KPI-First, One-Glance Design

9.5: Operational Dashboards: High Refresh, Drill-Down, Real-Time Monitoring

9.6: Presenting Power BI Findings: Structuring a BI Presentation

10.1: Use all Slicer Types: List, Dropdown, Relative Date, and Search

10.2: Sync Slicers Across Pages

10.3: Cross-Filtering, Cross-Highlighting, and Edit Interactions

10.4: Drill-Down, Drill-Through, and Drill-Up

10.5: Drill-Through Pages for Detailed Visual Exploration

10.6: Bookmarks for Toggle Views and Navigation

10.7: Tooltips: Default, Report Page Tooltips, and Custom Tooltip Pages

10.8: Buttons, Shapes, and Images for Custom Navigation

10.9: Q&A Visual and Natural Language Queries

10.10: Smart Narratives for Automated Summaries

10.11: Paginated Reports Introduction: When to Use Report Builder vs Desktop

10.12: User-Defined Functions

11.1: Publish PBIX Files to the Service and Understand What is Uploaded vs What Stays Local

11.2: Workspaces: Personal vs Collaborative Workspace Strategy

11.3: Licensing: Free, Pro, Premium Per User, and Fabric Capacity Explained

11.4: Sharing Reports: Direct Share, Links, Embedded, and App Publishing

11.5: Power BI Apps: Packaging and Distributing Reports to Business Users

11.6: Subscriptions and Alerts: Automated Report Delivery and Threshold Notifications

11.7: Scheduled Refresh: Setting up Automatic Data Updates

11.8: On-Premises Data Gateway: Connecting Cloud Reports to Local Data Sources

11.9: Deployment Pipelines: Dev → Test → Prod for Professional BI Release Management

11.10: Deployment Pipeline Rules: Parameterizing Connection Strings Across Environments

12.1: Understand how to protect business data and control what different users can see.

12.2: Why RLS is Required for Multi-User, Multi-Region Enterprise Reports

12.3: Static RLS: Creating Fixed Roles for Department-Level Access

12.4: Dynamic RLS: USERNAME() and USERPRINCIPALNAME() for User-Based Filtering

12.5: Testing RLS in Desktop and Service

12.6: Object-Level Security (OLS): Hiding Tables and Columns from Roles

12.7: Sensitivity Labels: Classifying and Protecting BI Assets

12.8: Endorsements: Certified, Promoted, and Discouraged Datasets

12.9: Power BI Admin Portal: Tenant Settings and Governance Configuration

13.1: Improve Dashboard Speed, Model Efficiency, and DAX performance for Large-Scale Reporting

13.2: Use Performance Analyzer to Measure Visual Rendering and DAX Query Execution Times

13.3: DAX Query View: Writing and Testing DAX Queries Directly

13.4: Reducing Model Size by Removing Unused Columns and Tables

13.5: Data Types and Cardinality: Choosing Integer Over String Keys

13.6: Aggregations: Pre-Aggregated Tables for Large Dataset Performance

13.7: Writing Performant DAX and Avoiding Common Anti-Patterns

13.8: VertiPaq Analyzer for Deep Model Analysis

14.1: Connect Power BI to Cloud-Native Platforms and Choose the Right Connectivity Approach for each Scenario.

14.2: Set up a DirectQuery Connection to Azure SQL Database With Row-Level Authentication

14.3: Connecting Power BI to Azure Synapse Analytics

14.4: Connecting Power BI to Azure Data Lake Storage Gen2 (ADLS)

14.5: Connecting Power BI to Snowflake: Import and DirectQuery Modes

14.6: Connecting Power BI to Google BigQuery

14.7: Connecting Power BI to AWS Redshift

14.8: Power BI Dataflows vs Direct Cloud Source Connections: Decision Framework

15.1: Understand how Power BI fits into the Modern Microsoft Analytics Ecosystem

15.2: Fabric Architecture and how Power BI Fits Within OneLake, Lakehouse, and Warehouses

15.3: Fabric Workspaces and Capacity: how Fabric Changes Power BI Licensing

15.4: OneLake: the Single Data Lake Underlying all Fabric Workloads

15.5: Direct Lake Storage Mode: Real-Time Performance Without Import Refresh

15.6: Creating a Power BI Semantic Model Over a Fabric Lakehouse

15.7: Fabric vs Traditional Power BI: When to Migrate and When to Stay

15.8: DP-600 Certification: Fabric Analytics Engineer Overview

16.1: Understand What Copilot Can and Cannot do in Power BI Desktop and Service

16.2: Copilot in Power BI Desktop: Generating DAX Measures With AI

16.3: Copilot Report Page Creation: Generating Visuals from a Description

16.4: Copilot Narrative Visual: AI-Generated Written Insights on Report Pages

16.5: Prompt Engineering for Power BI Copilot

16.6: Anomaly Detection on Line Charts: Automatic Outlier Identification

16.7: Smart Narratives vs Copilot Narratives: When to Use Each

16.8: Power BI + Power Automate: Triggering Workflows from Report Alerts

17.1: End-to-End Workflow: SQL Extraction → Power Query Cleaning → Star Schema → DAX KPIs → Interactive Dashboard

17.2: PL-300 Domain 1 Review: Prepare the Data

17.3: PL-300 Domain 2 Review: Model the Data

17.4: PL-300 Domain 3 Review: Visualize and Analyze the Data

17.5: PL-300 Domain 4 Review: Deploy and Maintain Assets

17.6: Building Your Power BI Portfolio: GitHub, Published Reports, and Demo Videos

17.7: Resume and LinkedIn Optimization for Power BI / BI Analyst Roles

17.8: Top 40 Power BI Interview Questions: Technical and Scenario-Based Answers

18.1: Project 01 — 365 Super Market (Analysis and Dashboard)

18.2: Project 02 — 365 Media Feathers (UI/ UX Designing)

18.3: Project 03 — 365 Granny's Café (Advanced Data Modelling)

18.4: Project 04 — 365 Financial Statements (Finance Core)

18.5: Project 05 — 365 Saleem Real Estate (Advanced Power BI Layouts)

18.6: Project 06 — 365 Business Banking (Banking Core)

18.7: Project 07 — 365 Human Resources (HR Core)

18.8: Project 08 — 365 Retail (Business Perspective)

18.9: Project 09 — 365 Merchandise (Business Perspective)

18.10: Project 10 — 365 Fitness Studio

18.11: Project 11 - 365 Pharmaceuticals (Advanced Formatting)

19.1: Project 01 — 365 Cricket Analytics

19.2: Project 02 — 365 Hospital Inc.

19.3: Project 03 — 365 Real Estate Inc.

19.4: Project 04 — 365 Mobile Tech Inc.

19.5: Project 05 — 365 Health Care Inc.

19.6: Project 06 — 365 Loan Default Analysis

19.7: Project 07 — 365 Inventory Management

19.8: Project 08 — 365 Fruita Vitals

19.9: Project 09 — 365 Insurance

19.10: Project 10 — 365 Sales Analytics