Course Introduction
Excel Power Query for Data Preparation and Automation Training Course provides a practical, structured approach to transforming Excel from a manually maintained spreadsheet environment into a repeatable data preparation and automation platform. Power Query, known as Get & Transform in Excel, enables users to connect to diverse data sources, reshape information, combine datasets, and refresh repeatable transformation processes without repeatedly performing the same manual cleaning activities.
This comprehensive Excel Power Query training course develops the skills required to import, clean, standardize, transform, and consolidate data efficiently. Participants learn how to work with Excel workbooks, CSV files, folders, web sources, databases, and other supported sources while building reusable queries that document transformation steps. The course emphasizes practical data preparation techniques that reduce repetitive work and improve consistency across recurring reporting and analytical processes.
A major focus of the training is the design of reliable data-cleaning workflows. Participants learn how to remove unnecessary rows and columns, correct data types, handle errors and missing values, split and combine columns, eliminate duplicates, standardize inconsistent values, and create calculated or conditional columns. Power Query records these transformations as applied steps, allowing the same process to be executed again when refreshed data becomes available.
The course also addresses advanced data-combination requirements that commonly arise in finance, operations, sales, human resources, procurement, and business reporting. Learners practice merging related datasets, appending files with consistent structures, grouping and aggregating information, pivoting and unpivoting data, and combining recurring files from folders. These capabilities are particularly useful for replacing repetitive copy-and-paste workflows with scalable and refreshable data preparation processes.
Automation and maintainability are central themes throughout the training. Participants learn how to structure queries, manage dependencies, parameterize transformations, create reusable custom functions, troubleshoot refresh problems, and introduce Power Query M expressions when the graphical interface is insufficient. Microsoft identifies M as the underlying transformation language used by Power Query, while the graphical interface automatically generates M code for many transformations.
By the end of the Excel Power Query for Data Preparation and Automation Training Course, participants will be able to build professional-grade data preparation workflows that are easier to refresh, audit, maintain, and reuse. The course combines hands-on exercises with practical business scenarios so that learners can apply Power Query to recurring reporting, data consolidation, operational analysis, management information, dashboard preparation, and other Excel-based data workflows. Microsoft also provides data profiling capabilities for understanding distributions, statistics, and row-level characteristics during preparation, supporting more systematic data-quality checks.
Duration
5 days
Who Should Attend
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Excel professionals responsible for preparing, cleaning, consolidating, and reporting business data.
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Data analysts who want to automate recurring Excel data preparation and transformation activities.
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Business analysts working with multiple spreadsheets, CSV files, databases, and external data sources.
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Finance professionals who consolidate recurring financial reports and management information.
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Operations analysts who combine operational datasets from different departments or systems.
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Reporting specialists who regularly refresh standardized reports and recurring analytical workbooks.
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Management information professionals responsible for producing accurate and repeatable reporting outputs.
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Accountants and finance teams seeking to reduce manual data consolidation and reconciliation processes.
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Sales and marketing analysts working with customer, transaction, campaign, and performance datasets.
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Human resources professionals preparing employee, recruitment, workforce, and payroll-related datasets.
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Data professionals transitioning from manual Excel workflows to repeatable ETL and data preparation processes.
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Excel power users seeking practical skills in Power Query automation, transformation, troubleshooting, and M.
Course Objectives
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Develop a strong understanding of Excel Power Query concepts, architecture, query workflows, and the role of repeatable data preparation in modern Excel-based analytics.
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Connect Excel Power Query to multiple data sources and confidently import structured and semi-structured information for recurring analysis and reporting requirements.
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Apply professional data-cleaning techniques to remove inconsistencies, correct data types, handle errors, eliminate duplicates, and prepare trustworthy datasets.
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Build repeatable transformation workflows using Power Query steps so that recurring data preparation activities can be refreshed without repeating manual spreadsheet operations.
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Combine datasets efficiently through merge and append operations while understanding keys, relationships, matching behavior, schema consistency, and common integration challenges.
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Transform complex datasets using filtering, grouping, pivoting, unpivoting, conditional logic, calculated columns, and text and date transformation techniques.
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Automate the consolidation of multiple similarly structured files by using folder-based queries and reusable transformation processes for recurring reporting environments.
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Use Power Query parameters, custom functions, and selected M expressions to create flexible, maintainable, and reusable data preparation solutions.
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Diagnose common query, transformation, source, data-type, and refresh problems while developing systematic approaches for validating and maintaining Power Query workflows.
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Design professional Power Query solutions that support reliable reporting, efficient refresh cycles, improved data quality, reduced manual effort, and sustainable Excel-based analytics.
Comprehensive Course Outline
Module 1: Introduction to Excel Power Query and Data Preparation
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Understanding Power Query, Get & Transform, data preparation, ETL concepts, and how repeatable queries improve Excel-based reporting workflows.
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Exploring the Power Query interface, Navigator, Query Editor, Applied Steps, Queries & Connections, data previews, and essential workflow components.
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Understanding the Connect, Transform, Combine, and Load stages and how they support structured data preparation and recurring refresh processes.
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Designing a practical Power Query workflow that separates source data, transformation logic, prepared datasets, and final Excel reporting outputs.
Module 2: Connecting Excel to Data Sources
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Importing data from Excel workbooks, tables, named ranges, CSV and text files, and other commonly used business data sources.
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Connecting to folders and SharePoint-based file locations for recurring datasets that require standardized consolidation and transformation.
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Exploring web, database, and external source connections while understanding authentication, connection settings, privacy considerations, and source dependencies.
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Evaluating source structures and selecting appropriate connectors to create stable, maintainable queries for recurring business data preparation.
Module 3: Data Cleaning and Quality Improvement
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Removing unnecessary columns and rows, promoting headers, replacing values, eliminating duplicates, and correcting common source-data inconsistencies.
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Managing missing values, errors, nulls, unexpected records, inconsistent categories, and invalid entries while preserving data-quality requirements.
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Applying correct data types for text, numbers, dates, times, currencies, percentages, and other fields before downstream calculations and analysis.
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Using Power Query data profiling capabilities to inspect distributions, statistics, column quality, and dataset characteristics during preparation.
Module 4: Transforming and Reshaping Data
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Splitting and merging columns, extracting text, changing case, trimming values, cleaning characters, and standardizing fields for reliable analysis.
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Creating conditional, custom, and example-based columns to derive business classifications, categories, flags, calculations, and analytical attributes.
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Pivoting and unpivoting datasets to convert cross-tabulated spreadsheet structures into analysis-ready tabular formats and flexible reporting structures.
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Applying filters, sorting, grouping, aggregation, indexing, and row-level transformations to create clean and purpose-built analytical datasets.
Module 5: Combining and Consolidating Data
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Understanding merge queries, join types, matching keys, duplicate keys, unmatched records, and relationship considerations when combining related datasets.
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Appending queries and consolidating datasets with consistent schemas from different periods, departments, branches, products, or business units.
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Combining multiple files from folders to automate recurring consolidation processes while applying common transformation logic to incoming files.
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Using grouping and aggregation to summarize transactional data into meaningful business measures such as totals, counts, averages, minimums, and maximums.
Module 6: Power Query Automation and Repeatable Workflows
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Designing refreshable queries that automatically rerun documented transformation steps whenever source data is updated or replaced.
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Building folder-based automation workflows for recurring monthly, weekly, daily, or departmental files with consistent preparation and consolidation requirements.
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Managing query dependencies, staging queries, reference queries, load settings, and transformation order to improve maintainability and workflow performance.
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Replacing repetitive copy-and-paste activities with structured Power Query processes that improve consistency, reduce manual errors, and accelerate reporting cycles.
Module 7: Parameters, Custom Functions, and M Language
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Understanding Power Query parameters and applying them to create flexible solutions for file paths, filters, reporting periods, business rules, and reusable configurations.
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Exploring the fundamentals of Power Query M, including expressions, values, functions, lists, records, tables, and the structure of transformation steps.
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Creating and invoking custom functions to standardize repeated transformation logic across files, tables, queries, or recurring business processes.
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Using the Advanced Editor to inspect, understand, modify, and troubleshoot M code generated by Power Query transformations.
Module 8: Advanced Data Preparation and Transformation
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Working with nested tables, lists, records, structured columns, and semi-structured data to prepare complex source information for analysis.
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Transforming JSON, XML, and other structured or semi-structured content into practical tabular datasets suitable for Excel reporting and analysis.
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Applying advanced text, date, numerical, conditional, and logical transformations to solve complex data-cleaning and business-rule requirements.
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Developing robust transformation sequences that account for changing source structures, inconsistent values, new records, and recurring data-quality issues.
Module 9: Refresh, Troubleshooting, and Performance Optimization
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Configuring and managing query refresh processes while understanding how Power Query reapplies transformation steps to updated source information.
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Diagnosing common errors involving missing columns, changed file structures, invalid data types, failed joins, authentication issues, and unavailable source locations.
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Reviewing query steps and transformation logic to identify unnecessary operations, improve maintainability, and reduce avoidable processing during refresh.
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Establishing validation and monitoring practices that help ensure refreshed datasets remain accurate, complete, consistent, and suitable for reporting.
Module 10: Building Production-Ready Excel Power Query Solutions
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Designing an end-to-end automated reporting workflow that connects raw sources, applies standardized transformations, combines datasets, and produces analysis-ready outputs.
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Applying naming conventions, documentation, query organization, dependency management, and reusable transformation patterns to improve long-term maintainability.
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Integrating prepared Power Query outputs with Excel tables, PivotTables, charts, dashboards, and other analytical components to support business decision-making.
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Developing a practical capstone solution that demonstrates automated data ingestion, cleansing, consolidation, transformation, refresh, validation, and professional reporting delivery.
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.
