Overview
This lecture covers the complete Power BI workflow from planning to sharing reports, highlighting the preparation, data modeling, visualization, and publishing processes using a new education-focused dataset.
Introduction & Planning a Power BI Project
- Begin all Power BI projects with clear goals and questions you want to answer.
- Assess data availability before starting—verify you have the right data to address your questions.
- Work collaboratively with subject matter experts to clarify needs and data context.
- Collect required tools and files: download Power BI Desktop (Windows only) and class files from GitHub or provided links.
Power BI Interface & Workflow Overview
- Power BI is organized by phases: data discovery (Get Data, Transform Data), data modeling, visualization, and sharing (publishing).
- Data is brought in via Get Data and cleaned using Power Query.
- Data modeling connects multiple data sources via relationships and enables calculations with DAX (Data Analysis Expressions).
- Visualizations are designed in the Report View; each report can have multiple pages and visuals.
- Reports are shared through the Power BI Service by publishing to the cloud.
Data Cleansing with Power Query
- Always clean and shape data in Power Query before building visuals.
- Replace blank values with nulls for accurate data transformation.
- Use "Fill Down" to propagate values in ragged hierarchies.
- Create new columns (e.g., city-state) with "Column from Examples."
- Data types must be correct for each column.
- Close & Apply to load cleaned data into the model.
Data Modeling & Relationships
- A strong data model improves performance, accuracy, scalability, and usability.
- Import multiple tables; relate them using primary/foreign keys (e.g., student ID).
- Hide key columns from report view to avoid confusion.
- Adjust summarization defaults (sum/average/none) for numerical columns.
- Use hierarchies to enable drill-down (e.g., state > district).
- Organize fields into folders for a tidy model.
- Create explicit measures using DAX for reusable calculations.
Visualization & Storytelling
- Start by identifying key questions to be answered with visuals.
- Limit visuals per page (typically 3-4) to avoid overwhelming users.
- Use formatting, themes, and conditional formatting to make data clear and accessible.
- Custom visuals and AI features like Q&A and Copilot help answer questions interactively and accelerate report building.
- Tooltips and drill-downs add depth without crowding the report surface.
Sharing & Power BI Service
- Publish your report to a workspace in the Power BI Service for collaboration and sharing.
- Share with individuals, groups, or through dashboards.
- Set up scheduled data refreshes to keep reports up-to-date.
- Editing is possible both in the desktop or web service, but maintaining one source of truth is best practice.
- Use dashboards to combine visuals from multiple reports for executive overviews.
Key Terms & Definitions
- Power Query — Tool in Power BI for data cleansing and transformation.
- Data Model — Structure connecting tables and defining relationships and calculations.
- DAX (Data Analysis Expressions) — Formula language for custom calculations.
- Explicit Measure — User-created calculation in DAX.
- Implicit Measure — Auto-generated calculation (e.g., sum, average) by Power BI.
- Hierarchy — Multi-level grouping for drill-down (e.g., year > month > day).
- Workspace — Storage area in Power BI Service for organizing and managing content.
- Semantic Model — The data model (tables, relationships, measures) published to the service.
Action Items / Next Steps
- Download and install Power BI Desktop (Windows required).
- Obtain the class files and datasets from provided links.
- Practice importing, cleaning, modeling, and visualizing the sample education dataset.
- Set up a Power BI Service workspace for publishing and sharing.
- Review recommended courses (beginner, modeling, DAX, accessibility) for deeper learning.
- Set up scheduled data refresh for live reports.