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This two-day intensive programme equips professionals with the practical skills to transform messy, inconsistent, and multi-source data into clean, structured, and analysis-ready datasets using Microsoft Power Query and AI tools. Designed for managers, analysts, operations teams, finance professionals, and anyone who regularly works with business data, the course focuses on building efficient and repeatable data preparation workflows that reduce manual effort and improve data accuracy.

Through practical exercises and real-world applications, participants will learn how to connect Power Query to multiple data sources, clean and standardise datasets, combine information from different files and systems, and perform advanced transformations such as pivoting, unpivoting, aggregating, and creating custom columns. The programme also introduces AI-assisted approaches for identifying errors, recognising data patterns, generating insights, and automating repetitive data-processing tasks.

Participants will also explore techniques for improving query performance, managing errors, understanding query dependencies, and using the Power Query M language to create custom functions and more flexible automated workflows. By the end of the programme, participants will be able to build reliable, refreshable data-processing solutions that turn raw information into structured datasets ready for reporting, analysis, and business decision-making.

What will you learn in Microsoft Power Query with AI Tools: Data Cleansing and Processing?

• Navigate Microsoft Power Query confidently, understanding its interface, workflow, and role in modern data preparation and processing.
• Connect and import data from multiple sources including Excel files, CSV files, databases, web sources, and other business systems, while using parameters to create more dynamic and flexible connections.
• Clean and standardise raw data efficiently by filtering and sorting records, removing duplicates, managing null values, correcting data types, and performing text and date transformations.
• Apply advanced Power Query transformations including custom and conditional columns, pivoting, unpivoting, aggregation, merging, and appending to prepare complex datasets for analysis.
• Leverage AI tools for data cleansing and processing, including intelligent error detection, pattern identification, data insights, and automation of repetitive tasks.
• Combine multiple datasets and manage query relationships by understanding join types, query dependencies, merging techniques, and integrated data workflows.
• Improve the reliability and performance of Power Query solutions through effective error handling, query optimisation, and best practices for working with larger datasets.
• Use Power Query M language for greater automation, including creating custom functions and extending Power Query beyond standard interface-based transformations.
• Build reusable and refreshable data workflows that integrate smoothly with Excel and reduce the time spent repeatedly preparing data for reporting and analysis.

Course Outline

Module 1: Connecting to Data Sources

– Importing data from various platforms including Excel, CSV, databases, web sources, and other business data sources
– Using parameters to create dynamic and flexible data connections
– Understanding query folding and its role in efficient data processing
– Establishing a strong foundation for repeatable and refreshable data workflows

Module 2: Basic Data Transformations

– Filtering and sorting data for better organisation and analysis
– Identifying and removing duplicate records
– Handling null and missing values effectively
– Managing and converting data types for greater consistency
– Introducing AI-powered approaches to error detection and correction

Module 3: Advanced Data Transformations

– Applying text transformations to clean and standardise inconsistent data
– Performing date transformations for accurate time-based analysis
– Creating custom columns using Power Query expressions
– Building conditional columns to apply business rules to datasets
– Using AI tools to identify data patterns and generate useful insights

Module 4: Combining Queries and Managing Relationships

– Understanding query dependencies and how different queries interact
– Understanding different join types and selecting the appropriate approach
– Merging queries to combine related datasets
– Appending queries to consolidate multiple datasets with similar structures
– Managing relationships between queries for more integrated data-processing workflows

Module 5: Error Handling and Query Optimisation

– Identifying common Power Query errors and understanding their causes
– Applying appropriate techniques to resolve data and transformation errors
– Optimising query performance when working with large datasets
– Applying best practices for building reliable, maintainable, and efficient data workflows

Module 6: Automating Processes with Power Query and AI Tools

– Introduction to the Power Query M language for more advanced customisation
– Creating custom functions to support reusable data-processing logic
– Automating repetitive data preparation tasks using AI-enhanced tools
– Integrating Power Query processes seamlessly into existing Excel workflows
– Developing more efficient, repeatable, and scalable approaches to data cleansing and processing