Power BI Data Modelling & DAX

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Course Description:

Data is everywhere, but are you maximizing its potential? The Power BI Course teaches you to transform ordinary data into valuable business insights that drive organizational success. The course equips data analysts and business intelligence professionals, as well as individuals seeking to enhance their Power BI Data Modelling and DAX skills.

The training offers practical experience for beginners and students preparing for the Power BI PL 300 Certification, focusing on efficient data model development alongside powerful DAX formula creation. Our premier Power BI Training Institute in Ahmedabad helps you gain expertise in dashboard development, report optimization, and data-driven decision-making skills.

What You Will Learn:

This MS Power BI Course provides you with usable skills that relate directly to actual business cases. Here’s what you’ll learn:

Fundamentals of Power BI Data Modelling for efficient data structuring
Creating relationships, hierarchies, and calculated tables
Mastering DAX for custom calculations and business logic
Designing interactive dashboards with advanced visualization techniques
Best practices for optimizing Power BI performance
Hands-on projects to reinforce your skills

Bascom Computer Education delivers the ultimate Microsoft Power BI training in Ahmedabad to help students develop field-tested abilities. Enrolling in our program will transform you into an expert Power BI user, so the next step in your professional growth begins now.

Curriculum

25 Sections
108 Lessons
10 Weeks

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Introduction to Power BI and Business Intelligence
5
- 1.1
  What is Business Intelligence (BI)?
- 1.2
  Role of a Data Analyst in an MNC.
- 1.3
  Introduction to Power BI: Components (Desktop, Service, Mobile, Report Builder).
- 1.4
  Why Power BI for Data Analysis? Key features and advantages.
- 1.5
  Power BI vs. other BI tools (e.g., Tableau, Excel for BI).
Power BI Desktop Interface
3
- 2.1
  Understanding the Power BI Desktop layout: Report View, Data View, Model View.
- 2.2
  Ribbon, Panes (Fields, Visualizations, Filters).
- 2.3
  Getting started: First steps and navigation.
Connecting to Data Sources
4
- 3.1
  Overview of various data sources: Excel, CSV, Text files.
- 3.2
  Connecting to databases: SQL Server, Access, Web data.
- 3.3
  Connecting to online services and cloud data sources.
- 3.4
  Import vs. DirectQuery vs. Live Connection: Understanding their differences and use cases.
Introduction to Power Query Editor (ETL Basics)
5
- 4.1
  Understanding the Extract, Transform, Load (ETL) process.
- 4.2
  Navigating the Power Query Editor interface.
- 4.3
  Basic data transformations: Renaming columns, changing data types, removing rows/columns.
- 4.4
  Handling missing values.
- 4.5
  Cleaning and reshaping data for analysis.
Basic Data Visualization
5
- 5.1
  Principles of effective data visualization.
- 5.2
  Creating basic charts: Bar charts, Column charts, Line charts, Pie charts.
- 5.3
  Understanding and using cards, tables, and matrices.
- 5.4
  Adding titles, labels, and legends.
- 5.5
  Introduction to filtering data in visuals (visual, page, and report level filters).
First Report Creation & Sharing Basics
3
- 6.1
  Building a simple interactive report.
- 6.2
  Saving and publishing reports to Power BI Service.
- 6.3
  Introduction to Power BI Service workspace.
Data Transformation & Modelling (Intermediate)
0
Advanced Power Query & Data Transformation
7
- 8.1
  Merging queries (Joins): Understanding different join types (Inner, Left, Right, Full, Anti).
- 8.2
  Appending queries.
- 8.3
  Grouping and aggregating data.
- 8.4
  Pivoting and Unpivoting columns.
- 8.5
  Creating custom columns using M language basics.
- 8.6
  Conditional columns and indexing.
- 8.7
  Data profiling and quality checks.
Data Modelling Fundamentals
6
- 9.1
  Importance of a robust data model.
- 9.2
  Understanding relationships: One-to-one, One-to-many, Many-to-many.
- 9.3
  Creating and managing relationships in Power BI.
- 9.4
  Star Schema vs. Snowflake Schema: Best practices for data modelling.
- 9.5
  Fact tables and Dimension tables.
- 9.6
  Optimizing data models for performance.
Introduction to Data Analysis Expressions (DAX)
5
- 10.1
  What is DAX? Calculated Columns vs. Measures.
- 10.2
  DAX syntax and common functions: SUM, AVERAGE, MIN, MAX, COUNT.
- 10.3
  Creating explicit measures for calculations.
- 10.4
  Understanding row context and filter context.
- 10.5
  Basic aggregation functions.
Intermediate Visualizations & Interactivity
6
- 11.1
  Advanced chart types: Scatter plots, Bubble charts, Area charts, Combo charts.
- 11.2
  Using Slicers for interactive filtering.
- 11.3
  Drill-through and Drill-down functionality.
- 11.4
  Bookmarks for guided storytelling.
- 11.5
  Conditional formatting for highlighting insights.
- 11.6
  Customizing themes and report appearance.
Report Design Best Practices
4
- 12.1
  Principles of effective dashboard design.
- 12.2
  Layout and aesthetics: Color palettes, fonts, spacing.
- 12.3
  Ensuring user-friendliness and clarity.
- 12.4
  Accessibility considerations in report design.
Advanced DAX & Data Storytelling (Advanced)
0
Advanced DAX Concepts
7
- 14.1
  CALCULATE function: The most powerful DAX function.
- 14.2
  Understanding context transition.
- 14.3
  Time intelligence functions: TOTALYTD, SAMEPERIODLASTYEAR, DATEADD, DATESBETWEEN.
- 14.4
  Creating complex measures: % of total, running totals, cumulative sums.
- 14.5
  Iterating functions: SUMX, AVERAGEX, COUNTX.
- 14.6
  Variables in DAX.
- 14.7
  Error handling in DAX.
Advanced Data Modelling Techniques
5
- 15.1
  Role-playing dimensions.
- 15.2
  Bridging tables for many-to-many relationships.
- 15.3
  Disconnected tables and what-if parameters.
- 15.4
  Handling hierarchies and ragged hierarchies.
- 15.5
  Performance optimization of DAX measures and data models.
Data Storytelling with Power BI
5
- 16.1
  Crafting a compelling narrative with data.
- 16.2
  Designing dashboards that tell a story.
- 16.3
  Using annotations and text boxes to guide the audience.
- 16.4
  Presenting insights to stakeholders effectively.
- 16.5
  Techniques for creating executive-level dashboards.
Power BI Service: Advanced Features & Administration
7
- 17.1
  Workspaces and app creation.
- 17.2
  Sharing and collaboration: Roles and permissions.
- 17.3
  Row-Level Security (RLS) implementation for data governance.
- 17.4
  Data gateways for connecting to on-premises data.
- 17.5
  Scheduled data refresh.
- 17.6
  Monitoring usage metrics and performance.
- 17.7
  Introduction to Power BI Apps and embedding reports.
Integrating Power BI with Other Tools
4
- 18.1
  Connecting Power BI to SQL Server Analysis Services (SSAS).
- 18.2
  Integration with Excel (Analyse in Excel).
- 18.3
  Overview of Power Automate and Power Apps integration.
- 18.4
  Introduction to custom visuals and R/Python integration in Power BI.
Real-World Applications & Capstone Project
0
Case Studies & Industry Applications
6
- 20.1
  Analysing sales data for a retail company.
- 20.2
  Financial reporting and budgeting with Power BI.
- 20.3
  HR analytics and workforce insights.
- 20.4
  Marketing campaign performance analysis.
- 20.5
  Supply chain and operations dashboards.
- 20.6
  Leveraging Power BI for specific industry problems (e.g., healthcare, manufacturing).
Performance Tuning and Optimization
5
- 21.1
  Identifying performance bottlenecks in Power BI reports.
- 21.2
  Optimizing data loading and transformation.
- 21.3
  Best practices for DAX query optimization.
- 21.4
  Efficient visualization design.
- 21.5
  Understanding Power BI data storage and refresh options.
Capstone Project
1
- 22.1
  Project Scope: Students will work on a real-world dataset (or a simulated complex dataset) from end-to-end.
Project Phases:
7
- 23.1
  Data Acquisition & Understanding: Identifying data sources, understanding business requirements.
- 23.2
  Data Preparation & Cleaning: Extensive use of Power Query for complex transformations.
- 23.3
  Data Modelling: Designing an optimized star/snowflake schema, defining relationships.
- 23.4
  DAX Calculations: Creating a comprehensive set of measures and calculated columns for business insights.
- 23.5
  Report & Dashboard Development: Designing interactive, insightful, and visually appealing reports and dashboards.
- 23.6
  Data Storytelling & Presentation: Presenting findings and recommendations to a “stakeholder” audience.
- 23.7
  Documentation: Documenting the data model, DAX measures, and design choices.
Preparing for the Certification Exam
4
- 24.1
  Exam objectives and structure.
- 24.2
  Key topics to focus on.
- 24.3
  Practice questions and mock exams.
- 24.4
  Study resources and tips.
Career Guidance for Data Analysts in MNCs
4
- 25.1
  Roles and responsibilities of a Data Analyst.
- 25.2
  In-demand skills beyond Power BI (SQL, Excel, Python/R basics).
- 25.3
  Resume building and interview preparation for data analyst roles.
- 25.4
  Networking and continuous learning in the data analytics field.

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