AI for Data Analysis with Power BI full Syllabus

Phase 1: Fundamentals of Data Analysis and Business Intelligence

1. Introduction to Data Analysis

• What is Data Analysis?
• Types of Data (Structured vs Unstructured)
• Role of a Data Analyst in Business
• Why Power BI? Industry use cases

2. Introduction to Power BI

• Power BI Overview: Desktop, Service, Mobile
• Installation & Setup of Power BI Desktop
• Navigating the Power BI Interface

Phase 2: Data Handling and Transformation (Power Query)

3. Getting Data into Power BI

• Connecting to various data sources (Excel, CSV, SQL, Web, APIs)
• Working with Power Query Editor
• Understanding Data Types and Formats

4. Data Cleaning and Shaping

• Removing errors, blanks, and duplicates
• Filtering, replacing, merging, and appending queries
• Creating custom columns and conditional logic
• Basic data transformation functions

5. Power BI Relationships & Data Modeling

• Introduction to data models
• Creating and managing relationships
• Star Schema vs Snowflake Schema
• Understanding cardinality and cross-filter direction

Phase 3: Data Analysis Expressions (DAX)

6. DAX Fundamentals

• What is DAX? Why is it important?
• Basic functions: SUM, AVERAGE, COUNT
• Calculated Columns vs Measures

7. Advanced DAX

• Time Intelligence functions (YTD, MTD, QTD)
• CALCULATE(), FILTER(), ALL(), RELATED()
• Variables in DAX
• Performance optimization tips

Phase 4: Visualizations & Dashboards

8. Visual Design Principles

• Choosing the right chart for the right data
• Color theory and minimalistic dashboard design
• Storytelling with Data

9. Creating Interactive Reports

• Charts: Bar, Line, Pie, Maps, KPIs, Waterfall, etc.
• Slicers, Filters, Bookmarks, and Drillthroughs
• Syncing and linking visuals
• Adding custom visuals from marketplace

10. Publishing and Sharing

• Power BI Service overview
• Workspaces, Dashboards, and Reports
• Sharing and embedding reports
• Power BI Mobile basics

Phase 5: Integrating Artificial Intelligence

11. AI Features in Power BI

• Smart Narratives (Auto-generated insights)
• Q&A Visual (Natural Language Query)
• Key Influencers Visual
• Decomposition Tree

12. Using Azure AI with Power BI

• Cognitive Services overview
• Sentiment Analysis and Language Detection
• Image tagging with Computer Vision APIs
• Integrating Azure ML models into Power BI

Phase 6: Automation & Real-Time Analysis

13. Power Automate Integration

• Trigger-based automation
• Alerts and workflows

14. Real-Time Dashboards

• Connecting to streaming datasets
• IoT and sensor data examples
• Use cases for real-time reporting

Phase 7: Projects & Case Studies

15. Hands-On Projects

• Sales & Marketing Dashboard
• HR Analytics Dashboard
• Finance Forecasting Model using DAX
• Customer Sentiment Analysis using AI

16. Final Capstone Project

• Define business problem
• Collect and clean data
• Build full Power BI dashboard
• Integrate AI feature
• Present actionable insights

Phase 8: Career & Certification Guidance

17. Resume Building and Interview Preparation

• Roles: Data Analyst, BI Developer, AI Analyst
• Portfolio building with GitHub or personal website
• Mock interview questions

18. Power BI Certification Prep (PL-300)

• Microsoft Certified: Data Analyst Associate
• Key topics and exam strategies
• Practice questions and time management