Summary

This was a Power BI DAX (Data Analysis Expressions) training session where the instructor taught participants how to create DAX formulas for business intelligence reporting. The class covered practical applications including creating filtered values, implementing static cards that don't interact with slicers, and building time intelligence measures for year-over-year and month-over-month comparisons. The instructor demonstrated three main approaches: using quick measures with filters, creating complex DAX formulas manually, and leveraging AI tools like ChatGPT and Claude to generate DAX formulas more efficiently. Key techniques included using the filtered value feature in quick measures, applying multiple filter conditions, and connecting date tables to enable time-based calculations. The session included hands-on exercises using the Northwind Traders dataset, with participants learning to create KPIs, star ratings for employee performance, and various financial metrics using both traditional DAX coding and AI-assisted approaches.

DAX Formulas in Power BI

The team discussed using DAX formulas in Power BI, focusing on practical applications rather than memorizing hundreds of formulas. They explored how to leverage AI tools like ChatGPT, Claude, and Copilot to assist with DAX questions by creating projects and uploading the DAX formula guide. The instructor demonstrated how to create multiple card visuals in Power BI and apply filters to show sales data for specific countries, explaining the difference between Power Query and DAX in handling cross-table operations.

Power BI Static Cards Training

The team took a break for prayer and agreed to reconvene at 6:30 PM. During the training session, the instructor demonstrated how to create static cards in Power BI that don't interact with slicers, using both visual filters and DAX measures. The instructor explained the concept of filtered values in DAX and showed how to create measures for specific countries like Argentina, including calculating metrics such as sum of net sales and average sales per quantity.

DAX KPI Formula Calculation Methods

The team discussed creating a DAX formula to calculate a KPI by dividing the sum of net sales in Argentina by the sum of quantity in Argentina. They demonstrated two methods: using quick measures and creating a single measure formula. The team confirmed they achieved the correct result of 24 using both approaches, with the single formula method being presented as more advanced and suitable for complex calculations. The session concluded with a suggestion to use AI tools like ChatGPT or Claude for generating DAX formulas, though it was noted that direct questions to these tools might not yield accurate results without proper context.

DAX Formulas with ChatGPT

The team discussed using ChatGPT to assist with creating DAX formulas in Power BI, particularly for calculating KPIs. They demonstrated how to use both Quick Measures and New Measures to apply filters, with Ashraf asking about using multiple filters simultaneously. The team showed how to create a DAX formula with two filter conditions (country and employee name) using the Calculate function, and explained that errors can be corrected using ChatGPT. They also mentioned that the PL300 exam focuses on basic DAX functions like Calculate and Sum with the Northwind traders dataset.

Power BI DAX Training Session

The team conducted a detailed Power BI training session focused on DAX measures and time intelligence functionality. They walked through creating star ratings for employees based on sales performance, implementing year-over-year and month-over-month calculations, and connecting date tables to create financial metrics. The instructor demonstrated how to use ChatGPT to generate DAX formulas and emphasized the importance of time intelligence measures for financial analysis. The session concluded with information about upcoming classes on data publishing scheduled for June 5th, and participants were informed they would receive certificates from Team Academy after completing the batch.