Get a comprehensive overview of the data end of Power BI—also known as Power Query—and learn how to use it to automate the data querying process and restructuring of data sets.
Overview
Syllabus
Introduction
- The Power BI ecosystem
- What is Power BI?
- Understanding ETL (extract, transform, and load)
- Focus on Power Query
- Course considerations
- Connecting to CSV or text files
- Manually entering data
- Connecting to an Excel file
- Connecting to a PDF file
- Connecting to folders
- Connecting to databases
- Comparing data connection modes
- Query folding and native queries
- Connecting to web tables
- Querying API data
- Querying REST API connections
- Configuring OData feeds
- Installing Python
- Running Python scripts
- Leveraging metadata
- Leveraging data types
- Making initial field transformations
- Splitting fields
- Merging fields
- Cleaning text fields
- Transforming numerical fields
- Removing or replacing values
- Filtering and removing duplicates
- Accessing native query in cleaning
- Introducing table objects
- Introducing list and record objects
- Working with binary objects
- Grouping data
- Pivoting data
- Transposing data
- Unpivoting data
- Accessing native query in integration
- Leveraging text formulas
- Conditional formulas
- Filling up or down columns
- Leveraging date formulas
- Combining binary files with formulas
- Accessing native query in enrichment
- Working with Query Editor steps
- Breaking down syntax
- Renaming steps in M
- Consolidating M steps
- Adding data types as custom M code
- Connecting to zipped binary data
- Utilizing parameters
- Creating list objects
- Referencing a list as a column in a table
- Leveraging record objects
- Leveraging list functions
- Creating date tables
- Looping with lists
- Combining list objects
- Setting up custom functions
- Converting queries into functions
- Configuring custom filtering
- Configuring loading options
- Fixing errors
- Refreshing data
- Joining sets of data
- Composite models
- Next steps
Taught by
Helen Wall
