This session focused on a hands-on Power BI case study, helping participants move beyond Excel and confidently analyze data using Power Query. Through practical examples, the team learned how to merge datasets, perform calculations, and transform raw data into meaningful insights.
๐ Case Study: Combining Multiple Excel Sheets
The session revolved around three key Excel datasets:
๐ฅ Employee Data
โฑ๏ธ Employee Attendance
๐ฐ Sales Data
Participants explored how to merge these datasets in Power BI using Employee ID as a common key, enabling deeper analysis such as:
Sales performance per employee
Working hours vs productivity
Employee-level insights across multiple data sources
This demonstrated how Power BI simplifies analysis that would otherwise be complex and time-consuming in Excel.
๐ Transform vs Add Column: A Core Power Query Concept
A key learning highlight was understanding the difference between:
โ๏ธ Transforming existing columns
(e.g., changing date formats, cleaning values)
โ Adding new columns
(e.g., calculating age, total hours, cost per sale)
Participants learned when to modify existing data and when to create new calculated columns based on business needs.
๐งฎ Practical Calculations & Business Logic
The class covered common but powerful calculations, including:
โฐ Calculating total working hours (Time Out โ Time In)
๐ Deriving employee age from date of birth
๐ Calculating cost per sale
๐ท๏ธ Creating conditional columns based on sales thresholds (Low, Medium, High)
It was emphasized that basic arithmetic operationsโaddition, subtraction, multiplication, and divisionโare sufficient for most real-world business scenarios.
๐ Data Integration & Validation Best Practices
Participants were guided on preparing Excel data correctly before loading it into Power BI, ensuring:
Correct data types
Valid date and time formats
Clean and consistent identifiers
This step is critical for accurate analysis and smooth query execution in Power BI.
โ๏ธ Advanced Power Query Techniques
The session also introduced advanced Power Query capabilities, including:
๐ Right outer joins and anti-joins for selective data extraction
๐ Unpivoting data to handle null values and reshape datasets
๐ Conditional logic for categorization and analysis
These techniques demonstrated how Power BI can efficiently handle large datasets and complex filtering requirements.
๐ Learning Support & Recap Session
To support deeper understanding, a follow-up recap class was announced for participants who wanted to revisit Power Query concepts. This ensures everyone can confidently apply transformations and calculations in real-world projects.
๐ Key Takeaways
Power BI simplifies multi-sheet Excel analysis ๐
Power Query enables powerful data transformations with minimal effort โ๏ธ
Understanding when to transform vs add columns is critical ๐ง
Clean, validated data leads to accurate insights โ
This session reinforced the transition from traditional Excel reporting to scalable, automated analytics using Power BI, empowering participants to analyze data more efficiently and confidently. ๐๐