Course Introduction
Data transformation is a critical stage in business intelligence because the quality, consistency and structure of data directly influence the accuracy of reports and analytical decisions. Organizations often collect information from multiple sources, including spreadsheets, databases, enterprise resource planning systems, cloud applications and external platforms. These datasets frequently contain duplicates, missing values, inconsistent formats, errors and structural differences that make analysis difficult. Microsoft Power Query provides powerful data extraction, transformation and loading capabilities that enable professionals to prepare raw information for reliable business intelligence. This course develops participants' practical skills in using Power Query within Microsoft Power BI to clean, reshape, combine and organize data for effective reporting and analysis.
The course introduces the Power Query Editor environment and its role within the broader Power BI data preparation process. Participants explore how to connect to different data sources, inspect data structures, assess quality and apply transformation operations to create analysis-ready datasets. They learn how to import information from Excel workbooks, CSV files, text documents, relational databases, SharePoint, cloud services and other supported sources. The programme also examines the extract, transform and load (ETL) process, helping participants understand how data moves from source systems into analytical models. Through practical exercises, participants develop repeatable preparation workflows that reduce manual processing and improve the consistency of reporting information.
A major component of the programme focuses on data cleaning and transformation techniques. Participants learn how to remove duplicates, handle missing values, correct data types, standardize text, replace errors and manage inconsistent records. The course explores techniques for splitting and merging columns, extracting information, replacing values, filtering records, sorting datasets and restructuring tables. Participants also examine how to work with dates, numbers, currencies, geographical fields and categorical variables. These capabilities are particularly important when preparing large or complex datasets for analysis, where small inconsistencies can create significant errors in calculations, relationships and visualizations.
The programme also develops advanced capabilities in data integration and transformation automation. Participants learn how to append and merge queries, combine files from folders, join tables using matching fields and consolidate information from multiple systems. They explore conditional columns, custom columns, grouping, pivoting, unpivoting and parameter-based transformations to address more complex data preparation requirements. The course introduces the M language, which powers Power Query transformations, and demonstrates how to inspect, understand and modify generated expressions. Participants also learn how to create reusable functions and parameter-driven workflows that make recurring data preparation processes more efficient, scalable and maintainable.
As organizational data environments become increasingly complex, effective transformation requires attention to performance, governance and emerging technologies. This course examines query folding, data source optimization, privacy levels, credential management and refresh considerations. Participants explore how Power Query interacts with cloud data platforms, enterprise systems and modern data architectures. The programme also considers contemporary developments such as dataflows, reusable transformation pipelines, Microsoft Fabric integration, automated data preparation and AI-assisted data processing. Particular emphasis is placed on maintaining data lineage, documenting transformation steps, validating outputs and implementing appropriate controls to protect sensitive information and ensure analytical reliability.
By the end of the course, participants will be able to use Power Query confidently to prepare high-quality datasets for Power BI reporting and analysis. They will understand how to build structured transformation workflows, integrate information from different sources, automate repetitive preparation tasks and troubleshoot common data quality challenges. The acquired skills will help organizations reduce manual spreadsheet work, improve reporting accuracy, shorten data preparation time and establish more reliable business intelligence processes. Participants will also be better equipped to maintain scalable and reusable data transformation solutions that support ongoing analytical and operational requirements.
Duration
5 days
Who Should Attend
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Power BI Developers and Report Developers
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Data Analysts and Business Intelligence Analysts
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Business Intelligence Managers and Specialists
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Database Administrators and Database Developers
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Data Engineers and Data Integration Specialists
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Management Information Systems Officers
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Financial Analysts and Reporting Professionals
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Business Analysts and Process Improvement Specialists
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IT Professionals and Systems Administrators
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Data Quality and Data Governance Officers
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Management Accountants and Financial Reporting Officers
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Research and Statistical Data Analysts
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ERP and Enterprise Application Administrators
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Data Warehouse and Reporting Specialists
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Consultants involved in business intelligence implementation
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Professionals responsible for Excel and spreadsheet automation
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Operations and Performance Management Analysts
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Data Science Professionals working with structured datasets
Course Objectives
By the end of this course, participants will be able to:
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Navigate the Power Query Editor and apply its data extraction, transformation and loading capabilities to prepare information for Microsoft Power BI analysis.
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Connect Power Query to multiple data sources, including Excel, CSV files, databases, SharePoint, cloud applications and enterprise information systems.
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Identify and correct common data quality problems involving missing values, duplicate records, inconsistent formats, invalid entries and incorrect data types.
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Apply advanced transformation techniques to split, merge, filter, group, pivot, unpivot and restructure datasets according to analytical and reporting requirements.
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Combine information from multiple tables and files using merge, append, join and folder-based consolidation techniques while preserving data integrity.
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Develop custom columns, conditional expressions, parameters and reusable functions to automate complex and repetitive data preparation processes.
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Understand and apply fundamental M language expressions to inspect, modify, troubleshoot and optimize Power Query transformation steps.
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Improve transformation performance by applying query folding principles, reducing unnecessary operations and optimizing data source interactions.
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Implement data validation, transformation documentation, privacy settings and refresh procedures to support reliable and secure data preparation workflows.
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Build scalable and reusable Power Query solutions that improve data consistency, reduce manual processing and strengthen organizational business intelligence capabilities.
Comprehensive Course Outline
Module 1: Introduction to Power Query and Data Transformation in Power BI
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Understanding the role of Power Query in the business intelligence lifecycle and its relationship with data extraction, transformation and loading processes.
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Exploring the Power Query Editor interface, query settings, applied steps, formula bar, data preview and transformation tools.
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Examining the differences between Power Query, Power BI data modeling, DAX calculations and other data preparation technologies.
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Identifying common data preparation challenges and establishing structured workflows for transforming raw information into analysis-ready datasets.
Module 2: Connecting to Data Sources and Importing Information
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Connecting Power Query to Excel workbooks, CSV files, text documents, JSON files and other common structured and semi-structured data sources.
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Importing data from SQL Server, relational databases, SharePoint, web sources and supported cloud-based business applications.
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Understanding connection settings, authentication methods, data source credentials and privacy levels when accessing organizational information.
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Exploring folder-based data imports and combining multiple files with similar structures into a consolidated reporting dataset.
Module 3: Data Cleaning, Standardization and Quality Management
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Identifying and removing duplicate records, handling blank values and correcting errors that affect data accuracy and analytical reliability.
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Standardizing text fields, numerical formats, dates, currencies and categorical values across inconsistent source datasets.
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Applying data type conversion, error replacement, null management and validation techniques to improve dataset consistency.
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Using column profiling, data distribution, column quality and data preview features to identify potential data quality problems.
Module 4: Advanced Data Transformation and Restructuring Techniques
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Applying split column, merge column, extract text, replace values and conditional transformation operations to restructure datasets.
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Filtering, sorting, grouping and aggregating records to create meaningful analytical datasets for reporting and visualization.
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Using pivot and unpivot operations to convert between wide and long data structures according to analytical requirements.
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Creating custom columns, conditional columns and index columns to support more complex data preparation and classification tasks.
Module 5: Combining, Merging and Integrating Multiple Data Sources
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Understanding the differences between merge queries, append queries and join operations when integrating information from multiple tables.
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Applying inner, left outer, right outer, full outer, anti and other join techniques to consolidate related datasets.
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Combining files from folders and integrating monthly, departmental or regional reports with consistent transformation rules.
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Resolving mismatched keys, inconsistent column names and differences in data structures when combining information from different systems.
Module 6: Introduction to M Language and Advanced Query Development
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Understanding the structure and purpose of the M language as the underlying formula language used by Power Query.
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Exploring M expressions, variables, data types, lists, records, tables and functions through practical transformation examples.
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Reading and modifying automatically generated M code using the Advanced Editor to customize data preparation processes.
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Developing custom functions and reusable transformation logic to standardize recurring data preparation activities across multiple datasets.
Module 7: Parameters, Automation and Reusable Transformation Workflows
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Creating and managing parameters to make data source connections, file paths and transformation settings more flexible.
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Developing parameter-driven queries that support reusable reporting solutions across different departments and business environments.
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Automating recurring data preparation operations through structured applied steps, functions and consistent transformation procedures.
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Designing scalable query structures that simplify maintenance, reduce repetitive work and support changing organizational reporting requirements.
Module 8: Query Optimization, Performance and Data Refresh Management
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Understanding query folding and how Power Query translates transformation steps into operations performed by supported data sources.
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Applying performance optimization techniques by filtering early, reducing unnecessary columns and avoiding inefficient transformation operations.
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Managing data source privacy levels, credentials, gateway requirements and refresh settings for reliable information access.
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Troubleshooting slow queries, refresh failures, data type conflicts and other performance problems affecting Power BI reporting workflows.
Module 9: Data Governance, Cloud Integration and Emerging Technologies
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Implementing data validation, query documentation, naming standards and transformation controls to improve data governance and traceability.
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Exploring Power BI dataflows and Microsoft Fabric capabilities for reusable data preparation and cloud-based transformation workflows.
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Understanding integration with enterprise data warehouses, cloud databases and modern data platforms for scalable analytical environments.
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Examining AI-assisted data preparation, automated data quality monitoring and emerging data engineering practices while considering security and privacy requirements.
Module 10: Practical Project, Troubleshooting and End-to-End Data Transformation
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Preparing a complete dataset by connecting to multiple source files, identifying quality issues and applying appropriate transformation techniques.
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Building an integrated query solution that combines, cleans and restructures data for financial, operational or management reporting.
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Developing reusable functions, parameters and automated transformation steps to address recurring data preparation requirements.
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Testing and validating transformation outputs, troubleshooting common errors and preparing a maintainable Power Query solution for Power BI reporting.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
