Later, you'll learn how to connect Power BI directly to SQL databases and other enterprise sources.

🔹 7. Importing Data

A typical process is:

Home

↓

Get Data

↓

Choose Source

↓

Select Table/File

↓

Transform Data

↓

Load

Don't immediately start creating charts.

First understand:

What data did I load?

🔹 8. Power Query

Power Query is Power BI's data preparation and transformation engine.

You'll use it to:

• Remove unwanted columns

• Rename columns

• Change data types

• Remove duplicates

• Handle missing values

• Split columns

• Merge tables

• Append tables

• Filter rows

• Create transformation steps

This is similar to the Power Query work you learned in Excel.

The important idea is:

Power Query prepares the data before analysis.

🔹 9. Power Query vs DAX

This distinction is extremely important.

Power Query

→ Used mainly for data preparation and transformation

DAX

→ Used mainly for calculations and analysis inside the data model

Think:

Power Query

"Prepare the data."

DAX

"Analyze the data."

You'll learn both in detail in later parts.

🔹 10. Data Modeling

Suppose you have:

Sales

• Order_ID

• Customer_ID

• Product_ID

• Date

• Sales

Customers

• Customer_ID

• Customer_Name

• Region

Products

• Product_ID

• Product_Name

• Category

Date

• Date

• Month

• Quarter

• Year

Instead of putting everything into one giant table, Power BI can connect these tables through relationships.

This is called data modeling.

🔹 11. Relationships

For example:

Customers

Customer_ID

↓

Sales

↑

Product_ID

Products

The relationship allows Power BI to understand how tables are connected.

For example:

Customer → Sales

allows you to analyze sales by customer region.

Product → Sales

allows you to analyze sales by product category.

🔹 12. Fact Tables and Dimension Tables

A common data-modeling structure is the star schema.

At the center:

⭐ Fact Table

Around it:

🔹 Dimension Tables

Example:

Customers

Products — Sales — Date

Region

The "Sales" table contains business events or measurements.

The dimension tables provide descriptive context.

This structure is extremely important for Power BI.

🔹 13. Measures vs Columns

Another fundamental concept.

Suppose you have:

"Sales"

A calculated column could calculate something for each row.

A measure calculates a value based on the current report context.

Example measure:

Total Sales =

SUM(Sales[Sales_Amount])

When you put this measure into a visual, Power BI calculates it according to the selected:

• Region

• Product

• Date

• Customer

• Filters

This makes measures extremely powerful.

🔹 14. Your First Visualization

Suppose you have:

Month| Sales

Jan| 100,000

Feb| 120,000

Mar| 150,000

You could create a line chart.

The chart immediately communicates:

📈 Sales are increasing over time.

But visualization choice matters.

You shouldn't select a chart because it looks attractive.

Choose it because it communicates the business message clearly.

🔹 15. Common Power BI Visuals

You should become familiar with:

📊 Bar Chart

📈 Line Chart

🥧 Pie / Donut Chart

🔢 Card

📋 Table

📑 Matrix

🎯 KPI

🗺️ Map

📊 Column Chart

🎛️ Slicer

Each visual serves a different analytical purpose.

🔹 16. Cards

Cards are useful for displaying important KPIs.