Introduction

R for M&E Data Analysis Training Course is designed to empower Monitoring and Evaluation professionals with cutting-edge analytical skills using R programming, the fastest-growing tool for data-driven decision-making. Participants will gain hands-on experience in data management, statistical analysis, visualization, and reporting, equipping them to translate raw data into insightful, actionable evidence. This course integrates modern M&E practices, real-world datasets, and case studies from international development programs to ensure learners can apply knowledge immediately in their professional contexts.

Through this training, participants will develop expertise in predictive analytics, trend analysis, and program performance evaluation. The course emphasizes practical learning, problem-solving, and critical thinking, ensuring participants master both technical R skills and their application to real-world M&E challenges. By the end, learners will be capable of designing dashboards, automating reports, and conducting robust statistical analyses, enabling evidence-based policy decisions and enhancing the impact of monitoring initiatives.

Programme Curriculum

R for M&E Data Analysis Training Course

Introduction

Course Duration

10 days

Course Objectives

By the end of this course, participants will be able to:

Use R programming for M&E data cleaning, transformation, and management.
Conduct descriptive and inferential statistical analyses in R.
Visualize complex datasets using ggplot2 and interactive dashboards.
Apply trend analysis and forecasting for program performance.
Automate report generation and reproducible research workflows.
Perform impact evaluation using regression and causal analysis.
Integrate data from multiple sources for comprehensive M&E reporting.
Develop data-driven insights for decision-making in monitoring frameworks.
Conduct program performance analytics using R packages.
Use time-series analysis for project monitoring.
Create interactive visualizations for stakeholder presentations.
Implement quality assurance and validation checks for M&E datasets.
Apply best practices for reproducible and ethical data analysis.

Target Audience

Monitoring & Evaluation Officers
Program Managers and Coordinators
Data Analysts in Development and Nonprofit Sectors
Policy Analysts and Government Evaluation Specialists
Research Assistants and Interns in M&E
NGO and International Organization Staff
Academic Researchers in Social and Development Studies
Any professional interested in enhancing data analytics skills for monitoring and evaluation

Course Modules

Module 1: Introduction to R for M&E

Understanding RStudio and R environment setup
Basic R syntax and operations
Data types and structures
Importing and exporting datasets
Case Study: Setting up R for a national health survey dataset

Module 2: Data Cleaning and Preprocessing

Handling missing data and outliers
Data transformation techniques
String manipulation and data formatting
Merging and reshaping datasets
Case Study: Cleaning household survey data for a nutrition program

Module 3: Descriptive Statistics

Summarizing datasets
Measures of central tendency and variability
Frequency tables and cross-tabulations
Exploratory data analysis
Case Study: Descriptive analysis of school enrollment data

Module 4: Data Visualization with ggplot2

Creating bar charts, line plots, and histograms
Customizing visualizations for reports
Using color and themes effectively
Advanced plotting techniques
Case Study: Visualizing vaccination coverage trends

Module 5: Advanced Data Visualization

Interactive dashboards with Shiny
Mapping with spatial data
Visual storytelling for stakeholders
Data dashboards for real-time monitoring
Case Study: Real-time M&E dashboard for a water project

Module 6: Inferential Statistics

Hypothesis testing and confidence intervals
t-tests, ANOVA, and chi-square tests
Correlation and covariance analysis
Reporting statistical findings
Case Study: Evaluating training program effectiveness

Module 7: Regression Analysis

Simple and multiple linear regression
Logistic regression for categorical outcomes
Model diagnostics and interpretation
Predictive analytics in M&E
Case Study: Predicting student performance based on interventions

Module 8: Time-Series Analysis

Trend detection and seasonal analysis
Forecasting program indicators
Visualization of temporal data
Application in project monitoring
Case Study: Monitoring monthly clinic visits over a year

Module 9: Impact Evaluation Techniques

Difference-in-differences
Propensity score matching
Randomized control trial basics
Causal inference for M&E
Case Study: Evaluating a community development program

Module 10: Data Integration and Management

Combining datasets from multiple sources
Using APIs and web scraping
Data version control with Git
Ensuring data consistency and integrity
Case Study: Integrating government and NGO datasets

Module 11: Automating Reports

R Markdown and reproducible reports
Automated dashboards and summaries
Custom templates for stakeholders
Streamlining M&E reporting workflows
Case Study: Automated monthly monitoring reports for a donor-funded program

Module 12: Quality Assurance in M&E Data

Validation techniques
Detecting inconsistencies and anomalies
Data audit trails
Ensuring compliance with standards
Case Study: QA process for survey-based evaluations

Module 13: Ethics and Best Practices in Data Analysis

Data privacy and confidentiality
Ethical data use in evaluation
Reproducible research practices
Compliance with international standards
Case Study: Ethical considerations in evaluating vulnerable populations

Module 14: Predictive Analytics for Decision-Making

Forecasting project outcomes
Risk analysis and scenario planning
Machine learning basics in R
Data-driven decision-making frameworks
Case Study: Predicting dropout rates in education programs

Module 15: Capstone Project & Real-World Application

Designing a full M&E data analysis project
From raw data to actionable insights
Reporting and presentation for stakeholders
Peer review and feedback
Case Study: Comprehensive evaluation of a multi-sector program

Training Methodology

This course employs a participatory and hands-on approach to ensure practical learning, including:

Interactive lectures and presentations.
Group discussions and brainstorming sessions.
Hands-on exercises using real-world datasets.
Role-playing and scenario-based simulations.
Analysis of case studies to bridge theory and practice.
Peer-to-peer learning and networking.
Expert-led Q&A sessions.
Continuous feedback and personalized guidance.

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

R programming Statistical analysis Data visualization Impact evaluation

Programme Fee

Duration: 10 Days

Method: Online or Physical

Online Price: $2,000

Physical Price: $3,000

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 30 Oct 2026

💻 Online / Virtual session

Online $2,000