Microsoft Power BI Data Analytics and Visualization

Day 1: Connecting, Shaping, and Modeling Data

Main Topics:

• Connecting to data sources (Excel, SQL, web)

• Power Query: cleaning and transforming data

• Star schema modeling

• Relationships and cardinality

Detailed Subtopics:

• Removing duplicates, splitting columns, and unpivoting data in Power Query

• Fact tables vs. dimension tables

• One-to-many vs. many-to-many relationships and cross-filter direction

• Building a date table for time intelligence

Hands-On Lab: Connect to a sample sales dataset (Excel and SQL), clean and shape it in Power Query, and build a star-schema model with a proper date table.

Real-World Scenario: A finance team's current 'reporting' is a single flat Excel sheet with inconsistent formatting that breaks every time someone edits it.

Learning Outcome: Participants can connect, clean, and model raw data into a proper, query-friendly star schema.

Day 2: DAX Calculations and Analysis

Main Topics:

• DAX fundamentals: calculated columns vs. measures

• Aggregation and filter context

• Time intelligence functions

• Common business calculations (YTD, running totals, variance)

Detailed Subtopics:

• CALCULATE and modifying filter context

• SUMX and other iterator functions

• Building YoY growth and YTD measures with time intelligence functions

• Debugging DAX with the correct mental model of context

Hands-On Lab: Write a set of DAX measures on the Day 1 model to calculate year-to-date sales, year-over-year growth, and a running total, and validate the results against a manual calculation.

Real-World Scenario: Leadership wants to see year-over-year growth trends by region, which the current flat Excel report cannot calculate reliably.

Learning Outcome: Participants can write correct, non-trivial DAX measures for real business analysis, not just simple aggregations.

Day 3: Visualization, Dashboards, and Publishing

Main Topics:

• Choosing the right visual for the data

• Report design and interactivity (slicers, drill-through)

• Publishing to the Power BI Service

• Row-level security and sharing

Detailed Subtopics:

• Common visualization mistakes and how to avoid them

• Building drill-through pages for detailed analysis

• Configuring scheduled refresh in the Power BI Service

• Implementing row-level security so users see only their own region/department data

Hands-On Lab: Build an interactive multi-page dashboard from the Day 2 model, publish it to the Power BI Service, configure scheduled refresh, and implement row-level security so a sales rep sees only their own region.

Real-World Scenario: The finance director wants regional sales managers to have access to the same dashboard but each should only see their own region's data.

Learning Outcome: Participants leave with a complete, published, secured interactive Power BI solution built from raw data.