Understanding the Power BI Desktop layout: Report View, Data View, Model View.

Ribbon, Panes (Fields, Visualizations, Filters).

Getting started: First steps and navigation.

Overview of various data sources: Excel, CSV, Text files.

Connecting to databases: SQL Server, Access, Web data.

Connecting to online services and cloud data sources.

Import vs. DirectQuery vs. Live Connection: Understanding their differences and use cases.

Understanding the Extract, Transform, Load (ETL) process.

Navigating the Power Query Editor interface.

Basic data transformations: Renaming columns, changing data types, removing rows/columns.

Handling missing values.

Cleaning and reshaping data for analysis.

Principles of effective data visualization.

Creating basic charts: Bar charts, Column charts, Line charts, Pie charts.

Understanding and using cards, tables, and matrices.

Adding titles, labels, and legends.

Introduction to filtering data in visuals (visual, page, and report level filters).

Building a simple interactive report.

Saving and publishing reports to Power BI Service.

Introduction to Power BI Service workspace.

Merging queries (Joins): Understanding different join types (Inner, Left, Right, Full, Anti).

Appending queries.

Grouping and aggregating data.

Pivoting and Unpivoting columns.

Creating custom columns using M language basics.

Conditional columns and indexing.

Data profiling and quality checks.

Importance of a robust data model.

Understanding relationships: One-to-one, One-to-many, Many-to-many.

Creating and managing relationships in Power BI.

Star Schema vs. Snowflake Schema: Best practices for data modelling.

Fact tables and Dimension tables.

Optimizing data models for performance.

What is DAX? Calculated Columns vs. Measures.

DAX syntax and common functions: SUM, AVERAGE, MIN, MAX, COUNT.

Creating explicit measures for calculations.

Understanding row context and filter context.

Basic aggregation functions.

Advanced chart types: Scatter plots, Bubble charts, Area charts, Combo charts.

Using Slicers for interactive filtering.

Drill-through and Drill-down functionality.

Bookmarks for guided storytelling.

Conditional formatting for highlighting insights.

Customizing themes and report appearance.

Principles of effective dashboard design.

Layout and aesthetics: Color palettes, fonts, spacing.

Ensuring user-friendliness and clarity.

Accessibility considerations in report design.

CALCULATE function: The most powerful DAX function.

Understanding context transition.

Time intelligence functions: TOTALYTD, SAMEPERIODLASTYEAR, DATEADD, DATESBETWEEN.

Creating complex measures: % of total, running totals, cumulative sums.

Iterating functions: SUMX, AVERAGEX, COUNTX.

Variables in DAX.

Error handling in DAX.

Role-playing dimensions.

Bridging tables for many-to-many relationships.

Disconnected tables and what-if parameters.

Handling hierarchies and ragged hierarchies.

Performance optimization of DAX measures and data models.

Crafting a compelling narrative with data.

Designing dashboards that tell a story.

Using annotations and text boxes to guide the audience.

Presenting insights to stakeholders effectively.

Techniques for creating executive-level dashboards.

Workspaces and app creation.

Sharing and collaboration: Roles and permissions.

Row-Level Security (RLS) implementation for data governance.

Data gateways for connecting to on-premises data.

Scheduled data refresh.

Monitoring usage metrics and performance.

Introduction to Power BI Apps and embedding reports.

Connecting Power BI to SQL Server Analysis Services (SSAS).

Integration with Excel (Analyse in Excel).

Overview of Power Automate and Power Apps integration.

Introduction to custom visuals and R/Python integration in Power BI.

Analysing sales data for a retail company.

Financial reporting and budgeting with Power BI.

HR analytics and workforce insights.

Marketing campaign performance analysis.

Supply chain and operations dashboards.

Leveraging Power BI for specific industry problems (e.g., healthcare, manufacturing).

Identifying performance bottlenecks in Power BI reports.

Optimizing data loading and transformation.

Best practices for DAX query optimization.

Efficient visualization design.

Understanding Power BI data storage and refresh options.

Project Scope: Students will work on a real-world dataset (or a simulated complex dataset) from end-to-end.

Data Acquisition & Understanding: Identifying data sources, understanding business requirements.

Data Preparation & Cleaning: Extensive use of Power Query for complex transformations.

Data Modelling: Designing an optimized star/snowflake schema, defining relationships.

DAX Calculations: Creating a comprehensive set of measures and calculated columns for business insights.

Report & Dashboard Development: Designing interactive, insightful, and visually appealing reports and dashboards.

Data Storytelling & Presentation: Presenting findings and recommendations to a “stakeholder” audience.

Documentation: Documenting the data model, DAX measures, and design choices.

Exam objectives and structure.

Key topics to focus on.

Practice questions and mock exams.

Study resources and tips.

Roles and responsibilities of a Data Analyst.

In-demand skills beyond Power BI (SQL, Excel, Python/R basics).

Resume building and interview preparation for data analyst roles.

Networking and continuous learning in the data analytics field.