The provided video covers a data analysis session focused on using Data Analysis Expressions (DAX) in Microsoft Power BI.

Session Summary

1. DAX Fundamentals:
   • DAX (Data Analysis Expressions) is a formula language used in Power BI for calculations, such as measures, calculated columns, and calculated tables.
   • It is essential for creating Key Performance Indicators (KPIs), performing aggregations, and building interactive dashboards.
   • DAX components include measures (performed at query time based on user interaction), calculated columns (row-by-row computations), and calculated tables.
   • DAX categories parallel those found in Excel, including aggregation, logical, text, and date functions.
   • Aggregation functions (e.g., SUM) operate on an entire column to return a single value, whereas iterator functions (e.g., SUMX) evaluate rows individually before aggregating.
2. Data Preparation and Modeling:
   • The instructor guided students through cleaning and loading five datasets: transactions, customers, accounts, branches, and loans.
   • Data cleaning involved checking for correct data types (e.g., changing amounts to fixed decimal numbers).
   • Data modeling followed a star schema, identifying a fact table and connecting dimension tables.
3. Practical DAX Exercises:
   • Students created measures for “Total Deposit” and “Total Withdrawal” using the SUM function.
   • A “Net Cash Flow” measure was created by subtracting total withdrawal from total deposit.
   • The instructor demonstrated using AI as a tool to generate and debug DAX syntax for more complex calculations, such as running totals.
4. Assignments:
   • Students were tasked with calculating the number of customers, average account balance, branch ranking by deposit, and average loan amount.