Live Course Module: Power BI Course for Data Science

Total Duration: 24 Hours (4 Weeks)

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Week 1 – Foundations of Power BI

Goal: Understand Power BI basics, setup, and working with datasets.

1. Introduction to Power BI & Data Science Role (2 hrs)

   • What is Power BI?

   • Importance in Data Science workflow

   • Power BI Desktop vs Service vs Mobile

   • Installing Power BI Desktop

2. Power BI Interface & Basic Workflow (2 hrs)

   • Home screen, ribbon, panes overview

   • Importing datasets (Excel, CSV, SQL, Web)

   • Understanding Queries & Models

3. Data Connections & ETL in Power BI (2 hrs)

   • Power Query basics

   • Connecting to databases & cloud sources

   • Data transformation steps (clean, filter, merge)

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Week 2 – Data Preparation & Modeling

Goal: Learn to clean, transform, and structure data for analysis.

4. Data Cleaning & Shaping with Power Query (2 hrs)

   • Removing duplicates & errors

   • Splitting/merging columns

   • Handling missing values

5. Data Modeling & Relationships (2 hrs)

   • Star vs Snowflake schema

   • Primary keys, relationships, cardinality

   • Building a data model for reporting

6. Introduction to DAX (Data Analysis Expressions) (2 hrs)

   • Calculated columns vs Measures

   • Basic DAX functions: SUM, AVERAGE, COUNT

   • Context in DAX (row vs filter)

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Week 3 – Advanced Analytics & Visualization

Goal: Create interactive dashboards with advanced analytics.

7. Advanced DAX for Data Science (2 hrs)

   • Time Intelligence functions (YTD, MTD, QTD)

   • IF, SWITCH, LOOKUP functions

   • Creating KPIs & measures

8. Data Visualization Best Practices (2 hrs)

   • Choosing right visuals for analysis

   • Bar, Line, Pie, Cards, Maps, Tables

   • Formatting visuals & conditional formatting

9. Interactive Dashboards & Filters (2 hrs)

   • Slicers, filters, drill-throughs

   • Hierarchies in visuals

   • Cross-report filtering

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Week 4 – Power BI in Data Science Projects

Goal: Apply Power BI for real-world data science & deploy solutions.

10. Integrating Power BI with Python & R (2 hrs)

• Running Python/R scripts in Power BI

• Data cleaning & ML model integration

• Visualization with Matplotlib/Seaborn inside Power BI

11. Publishing & Sharing Reports (2 hrs)

• Power BI Service overview

• Sharing dashboards, workspaces

• Row-level security (RLS) setup

12. Capstone Project & Review (2 hrs)

• Build an end-to-end project:

  • Data import → Cleaning → Modeling → DAX → Dashboard → Publishing

• Final Q&A & wrap-up

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✅ Final Outcome:

• Master Power BI Desktop & Service

• Build interactive dashboards from raw data

• Use DAX for advanced analytics

• Integrate Python/R for data science workflows

• Publish & share professional reports