Class Introduction
The session focused on Power Query in Power BI, covering data cleaning and transformation after data import. The trainer demonstrated grouping in Power Query using the Weekly Reports dataset, showing how to aggregate working hours and repairs by employee and how to handle null values. The class worked with PDF invoices, extracted tables, removed unnecessary columns, handled nulls, converted data types, and used Column from Example to clean and extract data like amounts and invoice numbers. The trainer explained the difference between Transform and Add Column tabs and showed date operations using a Timeline dataset to calculate task duration. The group also imported a CSV file from Northwind Traders, split the employee name into first and last names, merged columns, and created a custom full name column using M language. Finally, they added a calculated Total Sales column in the order details data using a custom formula.
Power Query Training Session
The team discussed Power Query training for Power BI, focusing on data cleaning and transformation techniques. The trainer guided participants through loading data from a folder and demonstrated how to group employee records by name to calculate total working hours. They covered the use of Power Query Editor features including the Group By function and explained how to handle null values in the data.
Related Offerings
Power BI Data Filtering Techniques
The team discussed Power BI data filtering and grouping techniques, focusing on correcting errors in Sahil's filtering setup and implementing advanced grouping with multiple outputs. They demonstrated how to group data by resource name while summing both total working hours and total repairs attended. The team then practiced refreshing Power Query datasets and importing PDF invoices, explaining how Power Query automatically identifies and extracts multiple tables from PDF files. The session concluded with an explanation of data types in Power Query, showing how to identify and view the data types for different columns in a table.
Power Query Data Transformation Techniques
The team discussed data cleaning and transformation techniques in Power Query. They removed the tax column and handled null values in the quantity column. The team then worked on grouping descriptions and calculating total unit prices, learning how to properly convert text values to numbers using column from example feature. Finally, they extracted and reorganized the invoice numbers column using the same technique, renaming it and moving it to the beginning of the table.
Power BI Data Transformations Training
The team conducted a Power BI training session focused on data transformations and column operations. They demonstrated how to drag and drop columns, explained the differences between transform and add column functions, and practiced date calculations by subtracting end dates from start dates to determine task durations. The session included hands-on exercises with CSV files, where participants learned to split columns using delimiters, merge columns, and create custom calculated columns using M language syntax. The instructor also showed how to calculate total sales by multiplying unit price, quantity, and applying a discount factor, emphasizing the importance of understanding data types and proper column formatting.
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