Course Introduction
Monitoring and Evaluation (M&E) professionals work with diverse datasets collected through surveys, field monitoring systems, administrative records, project management platforms, partner reports, and digital data collection tools. Preparing these datasets for analysis often involves repetitive manual tasks, inconsistent formatting, duplicated records, and time-consuming spreadsheet adjustments. This course equips participants with advanced Excel Power Query skills to automate data preparation, improve data consistency, and establish reliable workflows that support evidence-based programme management and evaluation.
The course provides a practical, structured approach to importing, cleaning, transforming, combining, and validating M&E data using Excel Power Query. Participants learn how to connect to multiple data sources, standardize variable names and data types, handle missing values, remove duplicates, correct inconsistencies, and restructure datasets for reporting and analysis. Emphasis is placed on building repeatable transformation steps that can be refreshed whenever new monitoring data becomes available, reducing manual intervention and improving the reliability of routine reporting processes.
A central component of the training is the application of Power Query to common monitoring and evaluation datasets, including indicator tracking tables, beneficiary registers, baseline and endline surveys, activity monitoring records, expenditure reports, partner submissions, and field verification datasets. Participants explore how to append datasets collected across different periods or locations, merge information using appropriate keys, reconcile inconsistent records, and organize data into analysis-ready structures. These practical exercises demonstrate how automated preparation can strengthen reporting timeliness and support more consistent interpretation of programme performance.
The course also addresses advanced data transformation techniques required when working with complex, high-volume, or frequently updated datasets. Participants learn to use conditional columns, custom columns, grouping and aggregation, pivoting and unpivoting, parameter-driven queries, reusable functions, and error-handling techniques. They are introduced to appropriate uses of the Power Query M language to customize transformations beyond the standard interface. The training emphasizes maintainability, traceability, and efficient query design so that data preparation processes remain understandable and adaptable as project requirements change.
Data quality assurance, confidentiality, and responsible data management are integrated throughout the programme. Participants learn to implement validation checks, identify unexpected values, detect duplicate beneficiaries, reconcile totals, investigate transformation errors, and document important data-cleaning decisions. The course highlights the importance of preserving source data, maintaining clear transformation histories, restricting access to sensitive beneficiary information, and applying consistent rules across reporting cycles. These practices help M&E teams strengthen data integrity while preparing datasets for dashboards, indicator calculations, evaluation analysis, and management reporting.
By the end of the training, participants will be equipped to design reusable Power Query workflows that turn fragmented raw data into standardized, analysis-ready information. Through practical exercises and realistic M&E scenarios, they will develop the confidence to automate routine preparation tasks, improve the reproducibility of analytical outputs, and reduce the time required to compile periodic reports. The skills gained can support more efficient monitoring systems, stronger data governance, better-quality evaluation evidence, and more timely decisions by programme managers, implementing partners, donors, and other stakeholders.
Duration
5 days
Who Should Attend
-
Monitoring and Evaluation officers responsible for preparing programme monitoring datasets and periodic performance reports.
-
M&E managers seeking to improve data preparation standards, reporting efficiency, and analytical quality across projects.
-
Data analysts who use Microsoft Excel to clean, combine, validate, and organize programme datasets.
-
Programme officers responsible for tracking activities, outputs, outcomes, targets, and performance indicators.
-
Evaluation specialists preparing baseline, midline, endline, and follow-up survey datasets for analysis.
-
Results-based management professionals working with results frameworks, indicator matrices, and performance measurement systems.
-
Project managers who need consistent, refreshed datasets to support implementation reviews and management decisions.
-
Research assistants and research officers preparing survey data and administrative records for statistical analysis.
-
Information management officers consolidating data from field offices, implementing partners, and multiple reporting systems.
-
NGO and development programme staff responsible for donor reporting and results documentation.
-
Public sector monitoring professionals handling departmental performance indicators and administrative data.
-
Data quality assurance personnel responsible for identifying inconsistencies, missing values, duplicates, and reporting errors.
-
Database and spreadsheet administrators maintaining recurring reporting templates and data consolidation processes.
-
Consultants supporting M&E system development, evaluation assignments, and programme data management improvements.
-
Professionals with foundational Excel knowledge who want to develop advanced, automated data preparation capabilities.
Course Objectives
By the end of this course, participants will be able to:
-
Design reusable Excel Power Query workflows that automate the importation, cleaning, transformation, and preparation of recurring monitoring and evaluation datasets.
-
Connect to Excel workbooks, CSV files, folders, tables, and other supported data sources while maintaining structured and refreshable data preparation processes.
-
Apply advanced data-cleaning techniques to standardize field names, correct data types, manage missing values, remove duplicates, and resolve inconsistent records.
-
Combine datasets from multiple reporting periods, locations, implementing partners, and collection systems using appropriate append and merge operations.
-
Transform indicator tracking tables, beneficiary registers, survey datasets, and activity records into structures suitable for performance analysis and reporting.
-
Use conditional columns, custom columns, grouping, aggregation, pivoting, unpivoting, and parameter-driven transformations to address complex M&E data requirements.
-
Understand and apply essential Power Query M language expressions to customize transformation logic and create reusable functions for recurring tasks.
-
Develop data quality checks that identify invalid values, unmatched records, duplicate identifiers, unexpected changes, and inconsistencies between reported totals.
-
Improve the efficiency, traceability, and maintainability of data preparation workflows through clear query naming, modular design, error handling, and documented transformation steps.
-
Produce standardized, analysis-ready datasets that can feed Excel reports, PivotTables, charts, dashboards, and subsequent statistical or evaluation analysis.
Comprehensive Course Outline
Module 1: Foundations of Power Query for Monitoring and Evaluation
-
Understanding the Power Query environment, its role in data preparation, and its advantages over repetitive manual spreadsheet cleaning.
-
Reviewing common M&E data structures, including indicator matrices, beneficiary registers, activity logs, survey extracts, and partner reporting templates.
-
Exploring the data preparation workflow from source identification and query creation to transformation, loading, and refresh management.
-
Establishing practical conventions for query names, source documentation, transformation steps, and reusable data preparation procedures.
Module 2: Connecting to Data Sources and Managing Data Imports
-
Importing structured Excel tables, worksheets, CSV files, text files, and other supported sources used in routine monitoring and evaluation reporting.
-
Connecting to folders containing recurring monthly, quarterly, or annual reports and preparing files for consolidated processing.
-
Managing source paths, headers, delimiters, encoding, data types, and other import settings that affect the accuracy of incoming datasets.
-
Configuring appropriate query properties, load destinations, and refresh settings to support reliable updates as new reporting files become available.
Module 3: Advanced Data Cleaning and Standardization
-
Standardizing column names, text values, dates, numerical fields, categorical variables, and identifier formats across inconsistent reporting submissions.
-
Identifying and treating blank cells, null values, invalid entries, duplicated records, extra spaces, and inconsistent spellings in programme datasets.
-
Applying conditional replacements, value transformations, text extraction, splitting, merging, and formatting rules to resolve common data quality problems.
-
Establishing repeatable cleaning rules that preserve source meaning, reduce avoidable data loss, and make transformation decisions transparent to reviewers.
Module 4: Combining and Reconciling M&E Datasets
-
Using append queries to consolidate similar monitoring datasets collected across reporting periods, project locations, field teams, and implementing partners.
-
Applying merge queries and appropriate join types to connect beneficiary records, activity data, indicator definitions, geographic references, and reporting tables.
-
Selecting reliable matching keys and diagnosing unmatched records, inconsistent identifiers, duplicate matches, and unexpected changes in row counts.
-
Reconciling consolidated datasets against source totals and documenting exceptions that require clarification from programme teams or data providers.
Module 5: Advanced Reshaping and Transformation Techniques
-
Using pivot and unpivot operations to restructure indicator tracking matrices and convert wide-format reporting tables into analysis-friendly layouts.
-
Applying grouping and aggregation techniques to summarize beneficiary counts, completed activities, expenditure records, and indicator results by relevant dimensions.
-
Creating conditional and custom columns to classify records, derive reporting categories, flag exceptions, and implement project-specific preparation rules.
-
Managing complex transformations involving multiple steps, nested tables, date attributes, category mappings, and standardized reporting structures.
Module 6: Introduction to Power Query M Language and Reusable Functions
-
Understanding the structure of M expressions, query steps, data types, lists, records, tables, and the Applied Steps sequence in the query editor.
-
Reading and modifying basic M expressions to customize cleaning rules, conditional logic, text processing, date handling, and data transformations.
-
Creating reusable functions and parameterized queries for processing similar datasets that vary by reporting period, source file, location, or project.
-
Applying defensive transformation techniques and error-handling logic to improve workflow resilience when incoming data contains unexpected values or structures.
Module 7: M&E Data Quality Assurance and Validation
-
Designing validation checks for missing identifiers, invalid indicator values, inconsistent categories, duplicate beneficiaries, and unexpected reporting periods.
-
Using filters, grouping, conditional flags, and exception tables to isolate records requiring correction, verification, or clarification from data owners.
-
Reconciling record counts, indicator totals, category distributions, and summary statistics before and after major transformation operations.
-
Documenting data-cleaning decisions, validation outcomes, unresolved issues, and quality limitations to support transparent reporting and evaluation analysis.
Module 8: Automating Recurring Reporting Workflows
-
Designing folder-based ingestion processes that consolidate similarly structured files and apply consistent transformation steps to newly received reports.
-
Configuring refreshable queries and managing changing source files, reporting periods, file names, and evolving data volumes in routine M&E workflows.
-
Organizing dependent queries into staging, transformation, and final output layers to improve clarity, maintainability, and troubleshooting efficiency.
-
Establishing practical refresh procedures and exception-handling routines that help teams maintain dependable monthly, quarterly, and annual reporting cycles.
Module 9: Preparing Data for Indicator Analysis and Reporting
-
Preparing analysis-ready datasets for indicator calculations, disaggregation by sex, age, geography, disability status where appropriate, and other relevant dimensions.
-
Consolidating baseline, follow-up, output monitoring, and outcome measurement records while preserving the identifiers and relationships needed for analysis.
-
Loading transformed data into Excel worksheets or the Data Model, where supported, for PivotTables, charts, summary tables, and reporting dashboards.
-
Applying final checks to ensure that prepared datasets align with indicator definitions, reporting periods, approved categories, and documented analytical requirements.
Module 10: Integrated Practical Project and Workflow Optimization
-
Developing an end-to-end Power Query solution using sample M&E data from multiple sources, including cleaning, standardization, consolidation, and validation.
-
Testing the workflow against new reporting files and deliberately introduced data issues to evaluate refresh reliability and error-handling behavior.
-
Reviewing query performance, transformation dependencies, naming conventions, documentation, and opportunities to simplify repetitive processing steps.
-
Presenting the completed workflow with a clear explanation of its quality controls, limitations, refresh process, and suitability for operational M&E 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.
